classificationdiabetes.ipynb

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   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import scale, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, mean_squared_error, r2_score, roc_auc_score, roc_curve, classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "df = pd.read_csv(\"diabetes.csv\")"
   ]
  },
  {
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    {
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       "    }\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>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",
       "      <td>0</td>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
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   "source": [
    "models = []\n",
    "\n",
    "models.append((\"LR\", LogisticRegression()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('SVR', SVC()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('RandomForests', RandomForestClassifier()))\n",
    "models.append(('GradientBoosting', GradientBoostingClassifier()))\n",
    "models.append(('XGBoost', XGBClassifier()))\n",
    "models.append(('Light GBM', LGBMClassifier()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Holdout Method: LR 0.7597402597402597\n",
      "Cross Val Score LR 0.7734791524265209\n",
      "------------------------------------\n",
      "Holdout Method: KNN 0.7077922077922078\n",
      "Cross Val Score KNN 0.7213773069036227\n",
      "------------------------------------\n",
      "Holdout Method: SVR 0.7727272727272727\n",
      "Cross Val Score SVR 0.7578605604921396\n",
      "------------------------------------\n",
      "Holdout Method: CART 0.6883116883116883\n",
      "Cross Val Score CART 0.7057245386192755\n",
      "------------------------------------\n",
      "Holdout Method: RandomForests 0.7272727272727273\n",
      "Cross Val Score RandomForests 0.7656698564593303\n",
      "------------------------------------\n",
      "Holdout Method: GradientBoosting 0.7532467532467533\n",
      "Cross Val Score GradientBoosting 0.7578092959671908\n",
      "------------------------------------\n",
      "Holdout Method: XGBoost 0.6883116883116883\n",
      "Cross Val Score XGBoost 0.7357142857142857\n",
      "------------------------------------\n",
      "Holdout Method: Light GBM 0.6883116883116883\n",
      "Cross Val Score Light GBM 0.7343984962406016\n",
      "------------------------------------\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(\"Outcome\",axis=1)\n",
    "y = df[\"Outcome\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=46)\n",
    "\n",
    "for name,model in models:\n",
    "    mod = model.fit(X_train,y_train) #trainleri modele fit etmek\n",
    "    y_pred = mod.predict(X_test) # tahmin\n",
    "    acc = accuracy_score(y_test, y_pred) #rmse hesabı\n",
    "    cvscore = cross_val_score(model, X,y, cv = 10).mean()\n",
    "    print(\"Holdout Method:\",end=\" \")\n",
    "    print(name,acc) #yazdırılacak kısım\n",
    "    print(\"Cross Val Score\",end=\" \")\n",
    "    print(name,cvscore)\n",
    "    print(\"------------------------------------\")"
   ]
  },
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   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Pregnancies</td>\n",
       "      <td>768.0</td>\n",
       "      <td>3.845052</td>\n",
       "      <td>3.369578</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Glucose</td>\n",
       "      <td>768.0</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>0.000</td>\n",
       "      <td>99.00000</td>\n",
       "      <td>117.0000</td>\n",
       "      <td>140.25000</td>\n",
       "      <td>199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BloodPressure</td>\n",
       "      <td>768.0</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>0.000</td>\n",
       "      <td>62.00000</td>\n",
       "      <td>72.0000</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>122.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SkinThickness</td>\n",
       "      <td>768.0</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Insulin</td>\n",
       "      <td>768.0</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>127.25000</td>\n",
       "      <td>846.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BMI</td>\n",
       "      <td>768.0</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.30000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>36.60000</td>\n",
       "      <td>67.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DiabetesPedigreeFunction</td>\n",
       "      <td>768.0</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.24375</td>\n",
       "      <td>0.3725</td>\n",
       "      <td>0.62625</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>768.0</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>21.000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Outcome</td>\n",
       "      <td>768.0</td>\n",
       "      <td>0.348958</td>\n",
       "      <td>0.476951</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count        mean         std     min       25%  \\\n",
       "Pregnancies               768.0    3.845052    3.369578   0.000   1.00000   \n",
       "Glucose                   768.0  120.894531   31.972618   0.000  99.00000   \n",
       "BloodPressure             768.0   69.105469   19.355807   0.000  62.00000   \n",
       "SkinThickness             768.0   20.536458   15.952218   0.000   0.00000   \n",
       "Insulin                   768.0   79.799479  115.244002   0.000   0.00000   \n",
       "BMI                       768.0   31.992578    7.884160   0.000  27.30000   \n",
       "DiabetesPedigreeFunction  768.0    0.471876    0.331329   0.078   0.24375   \n",
       "Age                       768.0   33.240885   11.760232  21.000  24.00000   \n",
       "Outcome                   768.0    0.348958    0.476951   0.000   0.00000   \n",
       "\n",
       "                               50%        75%     max  \n",
       "Pregnancies                 3.0000    6.00000   17.00  \n",
       "Glucose                   117.0000  140.25000  199.00  \n",
       "BloodPressure              72.0000   80.00000  122.00  \n",
       "SkinThickness              23.0000   32.00000   99.00  \n",
       "Insulin                    30.5000  127.25000  846.00  \n",
       "BMI                        32.0000   36.60000   67.10  \n",
       "DiabetesPedigreeFunction    0.3725    0.62625    2.42  \n",
       "Age                        29.0000   41.00000   81.00  \n",
       "Outcome                     0.0000    1.00000    1.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "df.describe().T"
   ]
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   "outputs": [],
   "source": [
    "df[[\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\"]] = df[[\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\"]].replace(0,np.NaN)"
   ]
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   0\n",
       "Glucose                       5\n",
       "BloodPressure                35\n",
       "SkinThickness               227\n",
       "Insulin                     374\n",
       "BMI                          11\n",
       "DiabetesPedigreeFunction      0\n",
       "Age                           0\n",
       "Outcome                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:40.727123Z",
     "iopub.status.busy": "2020-08-18T01:10:40.720885Z",
     "iopub.status.idle": "2020-08-18T01:10:40.771086Z",
     "shell.execute_reply": "2020-08-18T01:10:40.770165Z"
    },
    "papermill": {
     "duration": 0.15543,
     "end_time": "2020-08-18T01:10:40.771276",
     "exception": false,
     "start_time": "2020-08-18T01:10:40.615846",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "naValues = [\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\"]\n",
    "\n",
    "for i in naValues:\n",
    "    df[i][(df[i].isnull()) & (df[\"Outcome\"] == 0)] = df[i][(df[i].isnull()) & (df[\"Outcome\"] == 0)].fillna(df[i][df[\"Outcome\"] == 0].mean())\n",
    "    df[i][(df[i].isnull()) & (df[\"Outcome\"] == 1)] = df[i][(df[i].isnull()) & (df[\"Outcome\"] == 1)].fillna(df[i][df[\"Outcome\"] == 1].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:40.854740Z",
     "iopub.status.busy": "2020-08-18T01:10:40.853721Z",
     "iopub.status.idle": "2020-08-18T01:10:40.858632Z",
     "shell.execute_reply": "2020-08-18T01:10:40.857960Z"
    },
    "papermill": {
     "duration": 0.050374,
     "end_time": "2020-08-18T01:10:40.858785",
     "exception": false,
     "start_time": "2020-08-18T01:10:40.808411",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.049978,
     "end_time": "2020-08-18T01:10:40.951345",
     "exception": false,
     "start_time": "2020-08-18T01:10:40.901367",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "df[(df[\"Glucose\"].isnull()) & (df[\"Outcome\"] == 0)]\n",
    "\n",
    "df[\"Glucose\"][(df[\"Glucose\"].isnull()) & (df[\"Outcome\"] == 0)] = df[\"Glucose\"][(df[\"Glucose\"].isnull()) & (df[\"Outcome\"] == 0)].fillna(110.643863)\n",
    "\n",
    "df[\"Glucose\"][(df[\"Glucose\"].isnull()) & (df[\"Outcome\"] == 1)] = df[\"Glucose\"][(df[\"Glucose\"].isnull()) & (df[\"Outcome\"] == 1)].fillna(142.319549)\n",
    "\n",
    "#----------------------------------------------------------------\n",
    "\n",
    "df[\"BloodPressure\"][(df[\"BloodPressure\"].isnull()) & (df[\"Outcome\"] == 0)] = df[\"BloodPressure\"][(df[\"BloodPressure\"].isnull()) & (df[\"Outcome\"] == 0)].fillna(70.877339)\n",
    "\n",
    "df[\"BloodPressure\"][(df[\"BloodPressure\"].isnull()) & (df[\"Outcome\"] == 1)] = df[\"BloodPressure\"][(df[\"BloodPressure\"].isnull()) & (df[\"Outcome\"] == 1)].fillna(75.321429)\n",
    "\n",
    "#-----------------------------------------------------------------\n",
    "\n",
    "df[\"SkinThickness\"][(df[\"SkinThickness\"].isnull()) & (df[\"Outcome\"] == 0)] = df[\"SkinThickness\"][(df[\"SkinThickness\"].isnull()) & (df[\"Outcome\"] == 0)].fillna(27.235457)\n",
    "\n",
    "df[\"SkinThickness\"][(df[\"SkinThickness\"].isnull()) & (df[\"Outcome\"] == 1)] = df[\"SkinThickness\"][(df[\"SkinThickness\"].isnull()) & (df[\"Outcome\"] == 1)].fillna(33.000000)\n",
    "\n",
    "#-----------------------------------------------------------------\n",
    "\n",
    "df[\"Insulin\"][(df[\"Insulin\"].isnull()) & (df[\"Outcome\"] == 0)] = df[\"Insulin\"][(df[\"Insulin\"].isnull()) & (df[\"Outcome\"] == 0)].fillna(130.287879)\n",
    "\n",
    "df[\"Insulin\"][(df[\"Insulin\"].isnull()) & (df[\"Outcome\"] == 1)] = df[\"Insulin\"][(df[\"Insulin\"].isnull()) & (df[\"Outcome\"] == 1)].fillna(206.846154)\n",
    "\n",
    "#-----------------------------------------------------------------\n",
    "\n",
    "df[\"BMI\"][(df[\"BMI\"].isnull()) & (df[\"Outcome\"] == 0)] = df[\"BMI\"][(df[\"BMI\"].isnull()) & (df[\"Outcome\"] == 0)].fillna(30.859674)\n",
    "\n",
    "df[\"BMI\"][(df[\"BMI\"].isnull()) & (df[\"Outcome\"] == 1)] = df[\"BMI\"][(df[\"BMI\"].isnull()) & (df[\"Outcome\"] == 1)].fillna(35.406767)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:41.058650Z",
     "iopub.status.busy": "2020-08-18T01:10:41.057435Z",
     "iopub.status.idle": "2020-08-18T01:10:41.063393Z",
     "shell.execute_reply": "2020-08-18T01:10:41.062748Z"
    },
    "papermill": {
     "duration": 0.071536,
     "end_time": "2020-08-18T01:10:41.063573",
     "exception": false,
     "start_time": "2020-08-18T01:10:40.992037",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>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>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>206.846154</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>130.287879</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>206.846154</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>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.000000</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>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.000000</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    148.0           72.0           35.0  206.846154  33.6   \n",
       "1            1     85.0           66.0           29.0  130.287879  26.6   \n",
       "2            8    183.0           64.0           33.0  206.846154  23.3   \n",
       "3            1     89.0           66.0           23.0   94.000000  28.1   \n",
       "4            0    137.0           40.0           35.0  168.000000  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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:41.156024Z",
     "iopub.status.busy": "2020-08-18T01:10:41.154828Z",
     "iopub.status.idle": "2020-08-18T01:10:41.159549Z",
     "shell.execute_reply": "2020-08-18T01:10:41.158868Z"
    },
    "papermill": {
     "duration": 0.054951,
     "end_time": "2020-08-18T01:10:41.159685",
     "exception": false,
     "start_time": "2020-08-18T01:10:41.104734",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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    float64\n",
      " 2   BloodPressure             768 non-null    float64\n",
      " 3   SkinThickness             768 non-null    float64\n",
      " 4   Insulin                   768 non-null    float64\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(6), int64(3)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:41.244543Z",
     "iopub.status.busy": "2020-08-18T01:10:41.243553Z",
     "iopub.status.idle": "2020-08-18T01:10:41.247503Z",
     "shell.execute_reply": "2020-08-18T01:10:41.248131Z"
    },
    "papermill": {
     "duration": 0.049274,
     "end_time": "2020-08-18T01:10:41.248313",
     "exception": false,
     "start_time": "2020-08-18T01:10:41.199039",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:41.335070Z",
     "iopub.status.busy": "2020-08-18T01:10:41.333877Z",
     "iopub.status.idle": "2020-08-18T01:10:41.338623Z",
     "shell.execute_reply": "2020-08-18T01:10:41.337964Z"
    },
    "papermill": {
     "duration": 0.052392,
     "end_time": "2020-08-18T01:10:41.338754",
     "exception": false,
     "start_time": "2020-08-18T01:10:41.286362",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Outcome\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:41.421299Z",
     "iopub.status.busy": "2020-08-18T01:10:41.420480Z",
     "iopub.status.idle": "2020-08-18T01:10:41.424520Z",
     "shell.execute_reply": "2020-08-18T01:10:41.423882Z"
    },
    "papermill": {
     "duration": 0.047678,
     "end_time": "2020-08-18T01:10:41.424660",
     "exception": false,
     "start_time": "2020-08-18T01:10:41.376982",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:41.508143Z",
     "iopub.status.busy": "2020-08-18T01:10:41.507182Z",
     "iopub.status.idle": "2020-08-18T01:10:41.693333Z",
     "shell.execute_reply": "2020-08-18T01:10:41.692638Z"
    },
    "papermill": {
     "duration": 0.231184,
     "end_time": "2020-08-18T01:10:41.693467",
     "exception": false,
     "start_time": "2020-08-18T01:10:41.462283",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc35239790>"
      ]
     },
     "execution_count": 18,
     "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": [
    "df.Outcome.value_counts().plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:41.783657Z",
     "iopub.status.busy": "2020-08-18T01:10:41.780768Z",
     "iopub.status.idle": "2020-08-18T01:10:41.833755Z",
     "shell.execute_reply": "2020-08-18T01:10:41.833017Z"
    },
    "papermill": {
     "duration": 0.101818,
     "end_time": "2020-08-18T01:10:41.833915",
     "exception": false,
     "start_time": "2020-08-18T01:10:41.732097",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>1%</th>\n",
       "      <th>5%</th>\n",
       "      <th>10%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>80%</th>\n",
       "      <th>90%</th>\n",
       "      <th>95%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>768.0</td>\n",
       "      <td>3.845052</td>\n",
       "      <td>3.369578</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>768.0</td>\n",
       "      <td>121.697358</td>\n",
       "      <td>30.462008</td>\n",
       "      <td>44.000</td>\n",
       "      <td>67.67000</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>86.700</td>\n",
       "      <td>99.75000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>181.00000</td>\n",
       "      <td>196.00000</td>\n",
       "      <td>199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>768.0</td>\n",
       "      <td>72.428141</td>\n",
       "      <td>12.106044</td>\n",
       "      <td>24.000</td>\n",
       "      <td>44.00000</td>\n",
       "      <td>52.00000</td>\n",
       "      <td>58.000</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>88.0000</td>\n",
       "      <td>90.00000</td>\n",
       "      <td>106.00000</td>\n",
       "      <td>122.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>768.0</td>\n",
       "      <td>29.247042</td>\n",
       "      <td>8.923908</td>\n",
       "      <td>7.000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>14.35000</td>\n",
       "      <td>18.000</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>44.00000</td>\n",
       "      <td>51.33000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>768.0</td>\n",
       "      <td>157.003527</td>\n",
       "      <td>88.860914</td>\n",
       "      <td>14.000</td>\n",
       "      <td>24.34000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>121.50000</td>\n",
       "      <td>130.287879</td>\n",
       "      <td>206.846154</td>\n",
       "      <td>206.846154</td>\n",
       "      <td>210.0000</td>\n",
       "      <td>293.00000</td>\n",
       "      <td>519.90000</td>\n",
       "      <td>846.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>768.0</td>\n",
       "      <td>32.446420</td>\n",
       "      <td>6.878970</td>\n",
       "      <td>18.200</td>\n",
       "      <td>19.50000</td>\n",
       "      <td>22.23500</td>\n",
       "      <td>24.000</td>\n",
       "      <td>27.50000</td>\n",
       "      <td>32.050000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>37.800000</td>\n",
       "      <td>41.5000</td>\n",
       "      <td>44.39500</td>\n",
       "      <td>50.75900</td>\n",
       "      <td>67.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.09468</td>\n",
       "      <td>0.14035</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.24375</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>0.687000</td>\n",
       "      <td>0.8786</td>\n",
       "      <td>1.13285</td>\n",
       "      <td>1.69833</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>768.0</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>21.000</td>\n",
       "      <td>21.00000</td>\n",
       "      <td>21.00000</td>\n",
       "      <td>22.000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>42.600000</td>\n",
       "      <td>51.0000</td>\n",
       "      <td>58.00000</td>\n",
       "      <td>67.00000</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.348958</td>\n",
       "      <td>0.476951</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count        mean        std     min        1%  \\\n",
       "Pregnancies               768.0    3.845052   3.369578   0.000   0.00000   \n",
       "Glucose                   768.0  121.697358  30.462008  44.000  67.67000   \n",
       "BloodPressure             768.0   72.428141  12.106044  24.000  44.00000   \n",
       "SkinThickness             768.0   29.247042   8.923908   7.000  10.00000   \n",
       "Insulin                   768.0  157.003527  88.860914  14.000  24.34000   \n",
       "BMI                       768.0   32.446420   6.878970  18.200  19.50000   \n",
       "DiabetesPedigreeFunction  768.0    0.471876   0.331329   0.078   0.09468   \n",
       "Age                       768.0   33.240885  11.760232  21.000  21.00000   \n",
       "Outcome                   768.0    0.348958   0.476951   0.000   0.00000   \n",
       "\n",
       "                                5%     10%        25%         50%         75%  \\\n",
       "Pregnancies                0.00000   0.000    1.00000    3.000000    6.000000   \n",
       "Glucose                   80.00000  86.700   99.75000  117.000000  141.000000   \n",
       "BloodPressure             52.00000  58.000   64.00000   72.000000   80.000000   \n",
       "SkinThickness             14.35000  18.000   25.00000   28.000000   33.000000   \n",
       "Insulin                   50.00000  67.000  121.50000  130.287879  206.846154   \n",
       "BMI                       22.23500  24.000   27.50000   32.050000   36.600000   \n",
       "DiabetesPedigreeFunction   0.14035   0.165    0.24375    0.372500    0.626250   \n",
       "Age                       21.00000  22.000   24.00000   29.000000   41.000000   \n",
       "Outcome                    0.00000   0.000    0.00000    0.000000    1.000000   \n",
       "\n",
       "                                 80%       90%        95%        99%     max  \n",
       "Pregnancies                 7.000000    9.0000   10.00000   13.00000   17.00  \n",
       "Glucose                   147.000000  167.0000  181.00000  196.00000  199.00  \n",
       "BloodPressure              82.000000   88.0000   90.00000  106.00000  122.00  \n",
       "SkinThickness              35.000000   40.0000   44.00000   51.33000   99.00  \n",
       "Insulin                   206.846154  210.0000  293.00000  519.90000  846.00  \n",
       "BMI                        37.800000   41.5000   44.39500   50.75900   67.10  \n",
       "DiabetesPedigreeFunction    0.687000    0.8786    1.13285    1.69833    2.42  \n",
       "Age                        42.600000   51.0000   58.00000   67.00000   81.00  \n",
       "Outcome                     1.000000    1.0000    1.00000    1.00000    1.00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe([0.01,0.05,0.10,0.25,0.50,0.75,0.80,0.90,0.95,0.99]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:41.932391Z",
     "iopub.status.busy": "2020-08-18T01:10:41.931231Z",
     "iopub.status.idle": "2020-08-18T01:10:42.117786Z",
     "shell.execute_reply": "2020-08-18T01:10:42.117005Z"
    },
    "papermill": {
     "duration": 0.243837,
     "end_time": "2020-08-18T01:10:42.118074",
     "exception": false,
     "start_time": "2020-08-18T01:10:41.874237",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fcc3513e110>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.Glucose,df.Insulin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:42.218842Z",
     "iopub.status.busy": "2020-08-18T01:10:42.217701Z",
     "iopub.status.idle": "2020-08-18T01:10:42.423102Z",
     "shell.execute_reply": "2020-08-18T01:10:42.423709Z"
    },
    "papermill": {
     "duration": 0.266995,
     "end_time": "2020-08-18T01:10:42.423944",
     "exception": false,
     "start_time": "2020-08-18T01:10:42.156949",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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": [
    "sns.catplot(x = \"Outcome\", y = \"Glucose\", data = df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:42.520503Z",
     "iopub.status.busy": "2020-08-18T01:10:42.514856Z",
     "iopub.status.idle": "2020-08-18T01:10:42.704012Z",
     "shell.execute_reply": "2020-08-18T01:10:42.703153Z"
    },
    "papermill": {
     "duration": 0.240007,
     "end_time": "2020-08-18T01:10:42.704180",
     "exception": false,
     "start_time": "2020-08-18T01:10:42.464173",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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": [
    "sns.catplot(x = \"Outcome\", y = \"Insulin\", data = df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:42.801702Z",
     "iopub.status.busy": "2020-08-18T01:10:42.794428Z",
     "iopub.status.idle": "2020-08-18T01:10:42.994366Z",
     "shell.execute_reply": "2020-08-18T01:10:42.993435Z"
    },
    "papermill": {
     "duration": 0.250146,
     "end_time": "2020-08-18T01:10:42.994512",
     "exception": false,
     "start_time": "2020-08-18T01:10:42.744366",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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": [
    "sns.catplot(x = \"Outcome\", y = \"BloodPressure\", data = df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:43.087647Z",
     "iopub.status.busy": "2020-08-18T01:10:43.086517Z",
     "iopub.status.idle": "2020-08-18T01:10:43.276282Z",
     "shell.execute_reply": "2020-08-18T01:10:43.275497Z"
    },
    "papermill": {
     "duration": 0.239074,
     "end_time": "2020-08-18T01:10:43.276417",
     "exception": false,
     "start_time": "2020-08-18T01:10:43.037343",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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": [
    "sns.catplot(x = \"Outcome\", y = \"BMI\", data = df);#the 0 value for outcome feature is more dense around 20 BMI. Healthy people are more generally 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:43.488300Z",
     "iopub.status.busy": "2020-08-18T01:10:43.487459Z",
     "iopub.status.idle": "2020-08-18T01:10:43.601429Z",
     "shell.execute_reply": "2020-08-18T01:10:43.601996Z"
    },
    "papermill": {
     "duration": 0.285616,
     "end_time": "2020-08-18T01:10:43.602203",
     "exception": false,
     "start_time": "2020-08-18T01:10:43.316587",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc3502a450>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6Ub32W28hhG+HENaFEDqV+xp5PYTwTdEfSVII4SNJvzCzr+aHviHpv0V/Cn4u6ffNbHX+e+0byv2epfH7U4ffUPYp91v/n0ra7/0bU483SX+k3I84ZySdyr/1SVoj6QeS3su/vyuyZn++Z+9ISnmfoY692qbP/gqC/nx23t+RlM5/Df2bpDvpz6L+jEg6J+ltSf+g3F84NHx/+KfIAOCEfwkHAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOPk/wyYSgw8SMlkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:43.692703Z",
     "iopub.status.busy": "2020-08-18T01:10:43.691902Z",
     "iopub.status.idle": "2020-08-18T01:10:43.712415Z",
     "shell.execute_reply": "2020-08-18T01:10:43.711652Z"
    },
    "papermill": {
     "duration": 0.069306,
     "end_time": "2020-08-18T01:10:43.712570",
     "exception": false,
     "start_time": "2020-08-18T01:10:43.643264",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>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>Pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129867</td>\n",
       "      <td>0.208935</td>\n",
       "      <td>0.094172</td>\n",
       "      <td>0.093546</td>\n",
       "      <td>0.024127</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.129867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.224356</td>\n",
       "      <td>0.235040</td>\n",
       "      <td>0.497789</td>\n",
       "      <td>0.235253</td>\n",
       "      <td>0.138125</td>\n",
       "      <td>0.268566</td>\n",
       "      <td>0.495954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.208935</td>\n",
       "      <td>0.224356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203453</td>\n",
       "      <td>0.099996</td>\n",
       "      <td>0.286518</td>\n",
       "      <td>-0.002264</td>\n",
       "      <td>0.324439</td>\n",
       "      <td>0.175087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>0.094172</td>\n",
       "      <td>0.235040</td>\n",
       "      <td>0.203453</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.212573</td>\n",
       "      <td>0.565443</td>\n",
       "      <td>0.102426</td>\n",
       "      <td>0.135916</td>\n",
       "      <td>0.308094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>0.093546</td>\n",
       "      <td>0.497789</td>\n",
       "      <td>0.099996</td>\n",
       "      <td>0.212573</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.231533</td>\n",
       "      <td>0.121716</td>\n",
       "      <td>0.165149</td>\n",
       "      <td>0.410918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.024127</td>\n",
       "      <td>0.235253</td>\n",
       "      <td>0.286518</td>\n",
       "      <td>0.565443</td>\n",
       "      <td>0.231533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152530</td>\n",
       "      <td>0.027578</td>\n",
       "      <td>0.315271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.138125</td>\n",
       "      <td>-0.002264</td>\n",
       "      <td>0.102426</td>\n",
       "      <td>0.121716</td>\n",
       "      <td>0.152530</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.268566</td>\n",
       "      <td>0.324439</td>\n",
       "      <td>0.135916</td>\n",
       "      <td>0.165149</td>\n",
       "      <td>0.027578</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.495954</td>\n",
       "      <td>0.175087</td>\n",
       "      <td>0.308094</td>\n",
       "      <td>0.410918</td>\n",
       "      <td>0.315271</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
       "Pregnancies                  1.000000  0.129867       0.208935       0.094172   \n",
       "Glucose                      0.129867  1.000000       0.224356       0.235040   \n",
       "BloodPressure                0.208935  0.224356       1.000000       0.203453   \n",
       "SkinThickness                0.094172  0.235040       0.203453       1.000000   \n",
       "Insulin                      0.093546  0.497789       0.099996       0.212573   \n",
       "BMI                          0.024127  0.235253       0.286518       0.565443   \n",
       "DiabetesPedigreeFunction    -0.033523  0.138125      -0.002264       0.102426   \n",
       "Age                          0.544341  0.268566       0.324439       0.135916   \n",
       "Outcome                      0.221898  0.495954       0.175087       0.308094   \n",
       "\n",
       "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
       "Pregnancies               0.093546  0.024127                 -0.033523   \n",
       "Glucose                   0.497789  0.235253                  0.138125   \n",
       "BloodPressure             0.099996  0.286518                 -0.002264   \n",
       "SkinThickness             0.212573  0.565443                  0.102426   \n",
       "Insulin                   1.000000  0.231533                  0.121716   \n",
       "BMI                       0.231533  1.000000                  0.152530   \n",
       "DiabetesPedigreeFunction  0.121716  0.152530                  1.000000   \n",
       "Age                       0.165149  0.027578                  0.033561   \n",
       "Outcome                   0.410918  0.315271                  0.173844   \n",
       "\n",
       "                               Age   Outcome  \n",
       "Pregnancies               0.544341  0.221898  \n",
       "Glucose                   0.268566  0.495954  \n",
       "BloodPressure             0.324439  0.175087  \n",
       "SkinThickness             0.135916  0.308094  \n",
       "Insulin                   0.165149  0.410918  \n",
       "BMI                       0.027578  0.315271  \n",
       "DiabetesPedigreeFunction  0.033561  0.173844  \n",
       "Age                       1.000000  0.238356  \n",
       "Outcome                   0.238356  1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:43.808066Z",
     "iopub.status.busy": "2020-08-18T01:10:43.806514Z",
     "iopub.status.idle": "2020-08-18T01:10:44.434594Z",
     "shell.execute_reply": "2020-08-18T01:10:44.433892Z"
    },
    "papermill": {
     "duration": 0.681295,
     "end_time": "2020-08-18T01:10:44.434731",
     "exception": false,
     "start_time": "2020-08-18T01:10:43.753436",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc34f7e810>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize= [20,15])\n",
    "sns.heatmap(df.corr(),annot=True,ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.041837,
     "end_time": "2020-08-18T01:10:44.518617",
     "exception": false,
     "start_time": "2020-08-18T01:10:44.476780",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# ALGORITHMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:44.613521Z",
     "iopub.status.busy": "2020-08-18T01:10:44.611966Z",
     "iopub.status.idle": "2020-08-18T01:10:44.654019Z",
     "shell.execute_reply": "2020-08-18T01:10:44.654626Z"
    },
    "papermill": {
     "duration": 0.094346,
     "end_time": "2020-08-18T01:10:44.654810",
     "exception": false,
     "start_time": "2020-08-18T01:10:44.560464",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>768.0</td>\n",
       "      <td>3.845052</td>\n",
       "      <td>3.369578</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>768.0</td>\n",
       "      <td>121.697358</td>\n",
       "      <td>30.462008</td>\n",
       "      <td>44.000</td>\n",
       "      <td>99.75000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>768.0</td>\n",
       "      <td>72.428141</td>\n",
       "      <td>12.106044</td>\n",
       "      <td>24.000</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>122.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>768.0</td>\n",
       "      <td>29.247042</td>\n",
       "      <td>8.923908</td>\n",
       "      <td>7.000</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>768.0</td>\n",
       "      <td>157.003527</td>\n",
       "      <td>88.860914</td>\n",
       "      <td>14.000</td>\n",
       "      <td>121.50000</td>\n",
       "      <td>130.287879</td>\n",
       "      <td>206.846154</td>\n",
       "      <td>846.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>768.0</td>\n",
       "      <td>32.446420</td>\n",
       "      <td>6.878970</td>\n",
       "      <td>18.200</td>\n",
       "      <td>27.50000</td>\n",
       "      <td>32.050000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>67.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.24375</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>768.0</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>21.000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.348958</td>\n",
       "      <td>0.476951</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count        mean        std     min        25%  \\\n",
       "Pregnancies               768.0    3.845052   3.369578   0.000    1.00000   \n",
       "Glucose                   768.0  121.697358  30.462008  44.000   99.75000   \n",
       "BloodPressure             768.0   72.428141  12.106044  24.000   64.00000   \n",
       "SkinThickness             768.0   29.247042   8.923908   7.000   25.00000   \n",
       "Insulin                   768.0  157.003527  88.860914  14.000  121.50000   \n",
       "BMI                       768.0   32.446420   6.878970  18.200   27.50000   \n",
       "DiabetesPedigreeFunction  768.0    0.471876   0.331329   0.078    0.24375   \n",
       "Age                       768.0   33.240885  11.760232  21.000   24.00000   \n",
       "Outcome                   768.0    0.348958   0.476951   0.000    0.00000   \n",
       "\n",
       "                                 50%         75%     max  \n",
       "Pregnancies                 3.000000    6.000000   17.00  \n",
       "Glucose                   117.000000  141.000000  199.00  \n",
       "BloodPressure              72.000000   80.000000  122.00  \n",
       "SkinThickness              28.000000   33.000000   99.00  \n",
       "Insulin                   130.287879  206.846154  846.00  \n",
       "BMI                        32.050000   36.600000   67.10  \n",
       "DiabetesPedigreeFunction    0.372500    0.626250    2.42  \n",
       "Age                        29.000000   41.000000   81.00  \n",
       "Outcome                     0.000000    1.000000    1.00  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
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    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:44.753871Z",
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     "start_time": "2020-08-18T01:10:44.698204",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies no\n",
      "Glucose no\n",
      "BloodPressure no\n",
      "SkinThickness yes\n",
      "1\n",
      "lower -30.124999999999993 \n",
      "upper 88.475\n",
      "Insulin yes\n",
      "3\n",
      "lower -314.5 \n",
      "upper 657.5\n",
      "BMI no\n",
      "DiabetesPedigreeFunction no\n",
      "Age no\n",
      "Outcome no\n"
     ]
    }
   ],
   "source": [
    "for feature in df:\n",
    "\n",
    "    Q1 = df[feature].quantile(0.05)\n",
    "    Q3 = df[feature].quantile(0.95)\n",
    "    IQR = Q3-Q1\n",
    "    upper = Q3 + 1.5*IQR\n",
    "    lower = Q1 - 1.5*IQR\n",
    "\n",
    "    if df[(df[feature] > upper) | (df[feature] < lower)].any(axis=None):\n",
    "        print(feature,\"yes\")\n",
    "        print(df[(df[feature] > upper) | (df[feature] < lower)].shape[0])\n",
    "        print(\"lower\",lower,\"\\nupper\",upper)\n",
    "        df.loc[df[feature] > upper,feature] = upper\n",
    "    else:\n",
    "        print(feature, \"no\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:44.899079Z",
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>768.0</td>\n",
       "      <td>3.845052</td>\n",
       "      <td>3.369578</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>768.0</td>\n",
       "      <td>121.697358</td>\n",
       "      <td>30.462008</td>\n",
       "      <td>44.000</td>\n",
       "      <td>99.75000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>199.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>768.0</td>\n",
       "      <td>72.428141</td>\n",
       "      <td>12.106044</td>\n",
       "      <td>24.000</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>122.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>768.0</td>\n",
       "      <td>29.233338</td>\n",
       "      <td>8.824174</td>\n",
       "      <td>7.000</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>88.475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>768.0</td>\n",
       "      <td>156.616157</td>\n",
       "      <td>86.319814</td>\n",
       "      <td>14.000</td>\n",
       "      <td>121.50000</td>\n",
       "      <td>130.287879</td>\n",
       "      <td>206.846154</td>\n",
       "      <td>657.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>768.0</td>\n",
       "      <td>32.446420</td>\n",
       "      <td>6.878970</td>\n",
       "      <td>18.200</td>\n",
       "      <td>27.50000</td>\n",
       "      <td>32.050000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>67.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.24375</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>2.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>768.0</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>21.000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>81.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.348958</td>\n",
       "      <td>0.476951</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count        mean        std     min        25%  \\\n",
       "Pregnancies               768.0    3.845052   3.369578   0.000    1.00000   \n",
       "Glucose                   768.0  121.697358  30.462008  44.000   99.75000   \n",
       "BloodPressure             768.0   72.428141  12.106044  24.000   64.00000   \n",
       "SkinThickness             768.0   29.233338   8.824174   7.000   25.00000   \n",
       "Insulin                   768.0  156.616157  86.319814  14.000  121.50000   \n",
       "BMI                       768.0   32.446420   6.878970  18.200   27.50000   \n",
       "DiabetesPedigreeFunction  768.0    0.471876   0.331329   0.078    0.24375   \n",
       "Age                       768.0   33.240885  11.760232  21.000   24.00000   \n",
       "Outcome                   768.0    0.348958   0.476951   0.000    0.00000   \n",
       "\n",
       "                                 50%         75%      max  \n",
       "Pregnancies                 3.000000    6.000000   17.000  \n",
       "Glucose                   117.000000  141.000000  199.000  \n",
       "BloodPressure              72.000000   80.000000  122.000  \n",
       "SkinThickness              28.000000   33.000000   88.475  \n",
       "Insulin                   130.287879  206.846154  657.500  \n",
       "BMI                        32.050000   36.600000   67.100  \n",
       "DiabetesPedigreeFunction    0.372500    0.626250    2.420  \n",
       "Age                        29.000000   41.000000   81.000  \n",
       "Outcome                     0.000000    1.000000    1.000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:45.104614Z",
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     "iopub.status.idle": "2020-08-18T01:10:45.108514Z",
     "shell.execute_reply": "2020-08-18T01:10:45.107857Z"
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>206.846154</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>130.287879</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>206.846154</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>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.000000</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>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.000000</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    148.0           72.0           35.0  206.846154  33.6   \n",
       "1            1     85.0           66.0           29.0  130.287879  26.6   \n",
       "2            8    183.0           64.0           33.0  206.846154  23.3   \n",
       "3            1     89.0           66.0           23.0   94.000000  28.1   \n",
       "4            0    137.0           40.0           35.0  168.000000  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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:45.293736Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['BMIRanges'] = pd.cut(x=df['BMI'], bins=[0,18.5,25,30,100],labels = [\"Underweight\",\"Healthy\",\"Overweight\",\"Obese\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
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    "execution": {
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   "outputs": [
    {
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       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
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       "      <td>6</td>\n",
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       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>206.846154</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
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       "      <td>85.0</td>\n",
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       "      <td>29.0</td>\n",
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       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
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       "      <td>0</td>\n",
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       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>33.0</td>\n",
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       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
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       "      <td>Healthy</td>\n",
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       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>Overweight</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness     Insulin   BMI  \\\n",
       "0            6    148.0           72.0           35.0  206.846154  33.6   \n",
       "1            1     85.0           66.0           29.0  130.287879  26.6   \n",
       "2            8    183.0           64.0           33.0  206.846154  23.3   \n",
       "3            1     89.0           66.0           23.0   94.000000  28.1   \n",
       "4            0    137.0           40.0           35.0  168.000000  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome   BMIRanges  \n",
       "0                     0.627   50        1       Obese  \n",
       "1                     0.351   31        0  Overweight  \n",
       "2                     0.672   32        1     Healthy  \n",
       "3                     0.167   21        0  Overweight  \n",
       "4                     2.288   33        1       Obese  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
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       "      <th>Underweight</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>95.250000</td>\n",
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       "      <td>0.59250</td>\n",
       "      <td>0.624</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>3.464102</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
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       "    <tr>\n",
       "      <th>Healthy</th>\n",
       "      <td>101.0</td>\n",
       "      <td>3.138614</td>\n",
       "      <td>2.959830</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>104.689543</td>\n",
       "      <td>...</td>\n",
       "      <td>0.49100</td>\n",
       "      <td>1.698</td>\n",
       "      <td>101.0</td>\n",
       "      <td>31.099010</td>\n",
       "      <td>12.903879</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>72.0</td>\n",
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       "    <tr>\n",
       "      <th>Overweight</th>\n",
       "      <td>136.0</td>\n",
       "      <td>3.279412</td>\n",
       "      <td>2.769386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>113.482675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.56800</td>\n",
       "      <td>1.699</td>\n",
       "      <td>136.0</td>\n",
       "      <td>31.360294</td>\n",
       "      <td>12.994969</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Obese</th>\n",
       "      <td>259.0</td>\n",
       "      <td>3.409266</td>\n",
       "      <td>3.173667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>111.712911</td>\n",
       "      <td>...</td>\n",
       "      <td>0.56500</td>\n",
       "      <td>2.329</td>\n",
       "      <td>259.0</td>\n",
       "      <td>31.247104</td>\n",
       "      <td>10.461110</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>Healthy</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.285714</td>\n",
       "      <td>2.811541</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>157.571429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.55950</td>\n",
       "      <td>0.678</td>\n",
       "      <td>7.0</td>\n",
       "      <td>44.285714</td>\n",
       "      <td>14.738999</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32.50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overweight</th>\n",
       "      <td>44.0</td>\n",
       "      <td>5.818182</td>\n",
       "      <td>3.315031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>131.840909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.72425</td>\n",
       "      <td>1.318</td>\n",
       "      <td>44.0</td>\n",
       "      <td>37.931818</td>\n",
       "      <td>9.879266</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.75</td>\n",
       "      <td>36.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Obese</th>\n",
       "      <td>217.0</td>\n",
       "      <td>4.658986</td>\n",
       "      <td>3.828962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>143.952254</td>\n",
       "      <td>...</td>\n",
       "      <td>0.74200</td>\n",
       "      <td>2.420</td>\n",
       "      <td>217.0</td>\n",
       "      <td>36.658986</td>\n",
       "      <td>11.010678</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>36.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>7 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pregnancies                                           \\\n",
       "                          count      mean       std  min   25%  50%  75%   \n",
       "Outcome BMIRanges                                                          \n",
       "0       Underweight         4.0  0.750000  0.500000  0.0  0.75  1.0  1.0   \n",
       "        Healthy           101.0  3.138614  2.959830  0.0  1.00  2.0  5.0   \n",
       "        Overweight        136.0  3.279412  2.769386  0.0  1.00  2.0  5.0   \n",
       "        Obese             259.0  3.409266  3.173667  0.0  1.00  2.0  5.0   \n",
       "1       Healthy             7.0  5.285714  2.811541  1.0  3.50  6.0  7.0   \n",
       "        Overweight         44.0  5.818182  3.315031  0.0  3.00  6.0  8.0   \n",
       "        Obese             217.0  4.658986  3.828962  0.0  1.00  4.0  7.0   \n",
       "\n",
       "                          Glucose              ... DiabetesPedigreeFunction  \\\n",
       "                      max   count        mean  ...                      75%   \n",
       "Outcome BMIRanges                              ...                            \n",
       "0       Underweight   1.0     4.0   95.250000  ...                  0.59250   \n",
       "        Healthy      13.0   101.0  104.689543  ...                  0.49100   \n",
       "        Overweight   12.0   136.0  113.482675  ...                  0.56800   \n",
       "        Obese        13.0   259.0  111.712911  ...                  0.56500   \n",
       "1       Healthy       9.0     7.0  157.571429  ...                  0.55950   \n",
       "        Overweight   13.0    44.0  131.840909  ...                  0.72425   \n",
       "        Obese        17.0   217.0  143.952254  ...                  0.74200   \n",
       "\n",
       "                              Age                                           \\\n",
       "                       max  count       mean        std   min    25%   50%   \n",
       "Outcome BMIRanges                                                            \n",
       "0       Underweight  0.624    4.0  24.000000   3.464102  21.0  21.00  24.0   \n",
       "        Healthy      1.698  101.0  31.099010  12.903879  21.0  22.00  26.0   \n",
       "        Overweight   1.699  136.0  31.360294  12.994969  21.0  22.00  27.0   \n",
       "        Obese        2.329  259.0  31.247104  10.461110  21.0  24.00  27.0   \n",
       "1       Healthy      0.678    7.0  44.285714  14.738999  23.0  32.50  50.0   \n",
       "        Overweight   1.318   44.0  37.931818   9.879266  22.0  29.75  36.0   \n",
       "        Obese        2.420  217.0  36.658986  11.010678  21.0  27.00  36.0   \n",
       "\n",
       "                                 \n",
       "                      75%   max  \n",
       "Outcome BMIRanges                \n",
       "0       Underweight  27.0  27.0  \n",
       "        Healthy      35.0  72.0  \n",
       "        Overweight   36.0  81.0  \n",
       "        Obese        38.0  69.0  \n",
       "1       Healthy      56.0  60.0  \n",
       "        Overweight   46.0  62.0  \n",
       "        Obese        43.0  70.0  \n",
       "\n",
       "[7 rows x 64 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"Outcome\",\"BMIRanges\"]).describe()"
   ]
  },
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       "      <td>29.0</td>\n",
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       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>Overweight</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>33.0</td>\n",
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       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
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       "      <td>28.1</td>\n",
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       "   Pregnancies  Glucose  BloodPressure  SkinThickness     Insulin   BMI  \\\n",
       "0            6    148.0           72.0           35.0  206.846154  33.6   \n",
       "1            1     85.0           66.0           29.0  130.287879  26.6   \n",
       "2            8    183.0           64.0           33.0  206.846154  23.3   \n",
       "3            1     89.0           66.0           23.0   94.000000  28.1   \n",
       "4            0    137.0           40.0           35.0  168.000000  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome   BMIRanges  \n",
       "0                     0.627   50        1       Obese  \n",
       "1                     0.351   31        0  Overweight  \n",
       "2                     0.672   32        1     Healthy  \n",
       "3                     0.167   21        0  Overweight  \n",
       "4                     2.288   33        1       Obese  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    768.000000\n",
       "mean     156.616157\n",
       "std       86.319814\n",
       "min       14.000000\n",
       "25%      121.500000\n",
       "50%      130.287879\n",
       "75%      206.846154\n",
       "max      657.500000\n",
       "Name: Insulin, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
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   ],
   "source": [
    "df[\"Insulin\"].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:46.130325Z",
     "iopub.status.busy": "2020-08-18T01:10:46.129088Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def set_insulin(row):\n",
    "    if row[\"Insulin\"] >= 16 and row[\"Insulin\"] <= 166:\n",
    "        return \"Normal\"\n",
    "    else:\n",
    "        return \"Abnormal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:46.231804Z",
     "iopub.status.busy": "2020-08-18T01:10:46.230652Z",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.assign(INSULIN_DESC=df.apply(set_insulin, axis=1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
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   "outputs": [
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness     Insulin   BMI  \\\n",
       "0            6    148.0           72.0           35.0  206.846154  33.6   \n",
       "1            1     85.0           66.0           29.0  130.287879  26.6   \n",
       "2            8    183.0           64.0           33.0  206.846154  23.3   \n",
       "3            1     89.0           66.0           23.0   94.000000  28.1   \n",
       "4            0    137.0           40.0           35.0  168.000000  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome   BMIRanges INSULIN_DESC  \n",
       "0                     0.627   50        1       Obese     Abnormal  \n",
       "1                     0.351   31        0  Overweight       Normal  \n",
       "2                     0.672   32        1     Healthy     Abnormal  \n",
       "3                     0.167   21        0  Overweight       Normal  \n",
       "4                     2.288   33        1       Obese     Abnormal  "
      ]
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     "execution_count": 39,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:46.570622Z",
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   "outputs": [],
   "source": [
    "df['NewGlucose'] = pd.cut(x=df['Glucose'], bins=[0,70,99,126,200],labels = [\"Low\",\"Normal\",\"Secret\",\"High\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:46.687450Z",
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       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness     Insulin   BMI  \\\n",
       "0            6    148.0           72.0           35.0  206.846154  33.6   \n",
       "1            1     85.0           66.0           29.0  130.287879  26.6   \n",
       "2            8    183.0           64.0           33.0  206.846154  23.3   \n",
       "3            1     89.0           66.0           23.0   94.000000  28.1   \n",
       "4            0    137.0           40.0           35.0  168.000000  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome   BMIRanges INSULIN_DESC NewGlucose  \n",
       "0                     0.627   50        1       Obese     Abnormal       High  \n",
       "1                     0.351   31        0  Overweight       Normal     Normal  \n",
       "2                     0.672   32        1     Healthy     Abnormal       High  \n",
       "3                     0.167   21        0  Overweight       Normal     Normal  \n",
       "4                     2.288   33        1       Obese     Abnormal       High  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:46.803517Z",
     "iopub.status.busy": "2020-08-18T01:10:46.800438Z",
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:46.913697Z",
     "iopub.status.busy": "2020-08-18T01:10:46.912546Z",
     "iopub.status.idle": "2020-08-18T01:10:46.928094Z",
     "shell.execute_reply": "2020-08-18T01:10:46.927318Z"
    },
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     "duration": 0.074223,
     "end_time": "2020-08-18T01:10:46.928251",
     "exception": false,
     "start_time": "2020-08-18T01:10:46.854028",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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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",
       "      <th>BMIRanges_Healthy</th>\n",
       "      <th>BMIRanges_Overweight</th>\n",
       "      <th>BMIRanges_Obese</th>\n",
       "      <th>INSULIN_DESC_Normal</th>\n",
       "      <th>NewGlucose_Normal</th>\n",
       "      <th>NewGlucose_Secret</th>\n",
       "      <th>NewGlucose_High</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>206.846154</td>\n",
       "      <td>33.6</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>130.287879</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>206.846154</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness     Insulin   BMI  \\\n",
       "0            6    148.0           72.0           35.0  206.846154  33.6   \n",
       "1            1     85.0           66.0           29.0  130.287879  26.6   \n",
       "2            8    183.0           64.0           33.0  206.846154  23.3   \n",
       "3            1     89.0           66.0           23.0   94.000000  28.1   \n",
       "4            0    137.0           40.0           35.0  168.000000  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  BMIRanges_Healthy  \\\n",
       "0                     0.627   50        1                  0   \n",
       "1                     0.351   31        0                  0   \n",
       "2                     0.672   32        1                  1   \n",
       "3                     0.167   21        0                  0   \n",
       "4                     2.288   33        1                  0   \n",
       "\n",
       "   BMIRanges_Overweight  BMIRanges_Obese  INSULIN_DESC_Normal  \\\n",
       "0                     0                1                    0   \n",
       "1                     1                0                    1   \n",
       "2                     0                0                    0   \n",
       "3                     1                0                    1   \n",
       "4                     0                1                    0   \n",
       "\n",
       "   NewGlucose_Normal  NewGlucose_Secret  NewGlucose_High  \n",
       "0                  0                  0                1  \n",
       "1                  1                  0                0  \n",
       "2                  0                  0                1  \n",
       "3                  1                  0                0  \n",
       "4                  0                  0                1  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "papermill": {
     "duration": 0.04596,
     "end_time": "2020-08-18T01:10:47.021138",
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     "start_time": "2020-08-18T01:10:46.975178",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# robust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:47.121816Z",
     "iopub.status.busy": "2020-08-18T01:10:47.120771Z",
     "iopub.status.idle": "2020-08-18T01:10:47.124077Z",
     "shell.execute_reply": "2020-08-18T01:10:47.123343Z"
    },
    "papermill": {
     "duration": 0.055716,
     "end_time": "2020-08-18T01:10:47.124227",
     "exception": false,
     "start_time": "2020-08-18T01:10:47.068511",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:47.230750Z",
     "iopub.status.busy": "2020-08-18T01:10:47.229918Z",
     "iopub.status.idle": "2020-08-18T01:10:47.241760Z",
     "shell.execute_reply": "2020-08-18T01:10:47.241025Z"
    },
    "papermill": {
     "duration": 0.070747,
     "end_time": "2020-08-18T01:10:47.241896",
     "exception": false,
     "start_time": "2020-08-18T01:10:47.171149",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "r_scaler = RobustScaler()\n",
    "df_r = r_scaler.fit_transform(df.drop([\"Outcome\",\"BMIRanges_Healthy\",\"BMIRanges_Overweight\",\"BMIRanges_Obese\",\"INSULIN_DESC_Normal\",\"NewGlucose_Normal\",\"NewGlucose_Secret\",\"NewGlucose_High\"],axis=1))\n",
    "\n",
    "df_r = pd.DataFrame(df_r, columns=[\"Pregnancies\",\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\",\"DiabetesPedigreeFunction\",\"Age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:47.355522Z",
     "iopub.status.busy": "2020-08-18T01:10:47.353586Z",
     "iopub.status.idle": "2020-08-18T01:10:47.360313Z",
     "shell.execute_reply": "2020-08-18T01:10:47.360869Z"
    },
    "papermill": {
     "duration": 0.07274,
     "end_time": "2020-08-18T01:10:47.361056",
     "exception": false,
     "start_time": "2020-08-18T01:10:47.288316",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    .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>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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.751515</td>\n",
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       "      <td>0.875</td>\n",
       "      <td>0.897033</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>-0.500</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.897033</td>\n",
       "      <td>-0.961538</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.678788</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>-0.625</td>\n",
       "      <td>-0.425185</td>\n",
       "      <td>-0.434066</td>\n",
       "      <td>-0.537255</td>\n",
       "      <td>-0.470588</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.6</td>\n",
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       "      <td>-2.000</td>\n",
       "      <td>0.875</td>\n",
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       "      <th>763</th>\n",
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       "      <td>-0.387879</td>\n",
       "      <td>0.250</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.582476</td>\n",
       "      <td>0.093407</td>\n",
       "      <td>-0.526797</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.121212</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.521978</td>\n",
       "      <td>-0.084967</td>\n",
       "      <td>-0.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.096970</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.625</td>\n",
       "      <td>-0.214279</td>\n",
       "      <td>-0.642857</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>-0.750</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.897033</td>\n",
       "      <td>-0.214286</td>\n",
       "      <td>-0.061438</td>\n",
       "      <td>1.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.581818</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.181319</td>\n",
       "      <td>-0.150327</td>\n",
       "      <td>-0.352941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "0            0.6  0.751515          0.000          0.875  0.897033  0.170330   \n",
       "1           -0.4 -0.775758         -0.375          0.125  0.000000 -0.598901   \n",
       "2            1.0  1.600000         -0.500          0.625  0.897033 -0.961538   \n",
       "3           -0.4 -0.678788         -0.375         -0.625 -0.425185 -0.434066   \n",
       "4           -0.6  0.484848         -2.000          0.875  0.441873  1.214286   \n",
       "..           ...       ...            ...            ...       ...       ...   \n",
       "763          1.4 -0.387879          0.250          2.500  0.582476  0.093407   \n",
       "764         -0.2  0.121212         -0.125         -0.125  0.000000  0.521978   \n",
       "765          0.4  0.096970          0.000         -0.625 -0.214279 -0.642857   \n",
       "766         -0.4  0.218182         -0.750          0.625  0.897033 -0.214286   \n",
       "767         -0.4 -0.581818         -0.125          0.375  0.000000 -0.181319   \n",
       "\n",
       "     DiabetesPedigreeFunction       Age  \n",
       "0                    0.665359  1.235294  \n",
       "1                   -0.056209  0.117647  \n",
       "2                    0.783007  0.176471  \n",
       "3                   -0.537255 -0.470588  \n",
       "4                    5.007843  0.235294  \n",
       "..                        ...       ...  \n",
       "763                 -0.526797  2.000000  \n",
       "764                 -0.084967 -0.117647  \n",
       "765                 -0.333333  0.058824  \n",
       "766                 -0.061438  1.058824  \n",
       "767                 -0.150327 -0.352941  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:47.465008Z",
     "iopub.status.busy": "2020-08-18T01:10:47.463881Z",
     "iopub.status.idle": "2020-08-18T01:10:47.467637Z",
     "shell.execute_reply": "2020-08-18T01:10:47.466883Z"
    },
    "papermill": {
     "duration": 0.059677,
     "end_time": "2020-08-18T01:10:47.467769",
     "exception": false,
     "start_time": "2020-08-18T01:10:47.408092",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.concat([df_r,df[[\"Outcome\",\"BMIRanges_Healthy\",\"BMIRanges_Overweight\",\"BMIRanges_Obese\",\"INSULIN_DESC_Normal\",\"NewGlucose_Normal\",\"NewGlucose_Secret\",\"NewGlucose_High\"]]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:47.589814Z",
     "iopub.status.busy": "2020-08-18T01:10:47.575456Z",
     "iopub.status.idle": "2020-08-18T01:10:47.596868Z",
     "shell.execute_reply": "2020-08-18T01:10:47.596178Z"
    },
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     "duration": 0.082686,
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     "exception": false,
     "start_time": "2020-08-18T01:10:47.514328",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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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",
       "      <th>BMIRanges_Healthy</th>\n",
       "      <th>BMIRanges_Overweight</th>\n",
       "      <th>BMIRanges_Obese</th>\n",
       "      <th>INSULIN_DESC_Normal</th>\n",
       "      <th>NewGlucose_Normal</th>\n",
       "      <th>NewGlucose_Secret</th>\n",
       "      <th>NewGlucose_High</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.751515</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.897033</td>\n",
       "      <td>0.170330</td>\n",
       "      <td>0.665359</td>\n",
       "      <td>1.235294</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.775758</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.598901</td>\n",
       "      <td>-0.056209</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.897033</td>\n",
       "      <td>-0.961538</td>\n",
       "      <td>0.783007</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.678788</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>-0.625</td>\n",
       "      <td>-0.425185</td>\n",
       "      <td>-0.434066</td>\n",
       "      <td>-0.537255</td>\n",
       "      <td>-0.470588</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.441873</td>\n",
       "      <td>1.214286</td>\n",
       "      <td>5.007843</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.387879</td>\n",
       "      <td>0.250</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.582476</td>\n",
       "      <td>0.093407</td>\n",
       "      <td>-0.526797</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.121212</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.521978</td>\n",
       "      <td>-0.084967</td>\n",
       "      <td>-0.117647</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.096970</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.625</td>\n",
       "      <td>-0.214279</td>\n",
       "      <td>-0.642857</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>-0.750</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.897033</td>\n",
       "      <td>-0.214286</td>\n",
       "      <td>-0.061438</td>\n",
       "      <td>1.058824</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.581818</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.181319</td>\n",
       "      <td>-0.150327</td>\n",
       "      <td>-0.352941</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "0            0.6  0.751515          0.000          0.875  0.897033  0.170330   \n",
       "1           -0.4 -0.775758         -0.375          0.125  0.000000 -0.598901   \n",
       "2            1.0  1.600000         -0.500          0.625  0.897033 -0.961538   \n",
       "3           -0.4 -0.678788         -0.375         -0.625 -0.425185 -0.434066   \n",
       "4           -0.6  0.484848         -2.000          0.875  0.441873  1.214286   \n",
       "..           ...       ...            ...            ...       ...       ...   \n",
       "763          1.4 -0.387879          0.250          2.500  0.582476  0.093407   \n",
       "764         -0.2  0.121212         -0.125         -0.125  0.000000  0.521978   \n",
       "765          0.4  0.096970          0.000         -0.625 -0.214279 -0.642857   \n",
       "766         -0.4  0.218182         -0.750          0.625  0.897033 -0.214286   \n",
       "767         -0.4 -0.581818         -0.125          0.375  0.000000 -0.181319   \n",
       "\n",
       "     DiabetesPedigreeFunction       Age  Outcome  BMIRanges_Healthy  \\\n",
       "0                    0.665359  1.235294        1                  0   \n",
       "1                   -0.056209  0.117647        0                  0   \n",
       "2                    0.783007  0.176471        1                  1   \n",
       "3                   -0.537255 -0.470588        0                  0   \n",
       "4                    5.007843  0.235294        1                  0   \n",
       "..                        ...       ...      ...                ...   \n",
       "763                 -0.526797  2.000000        0                  0   \n",
       "764                 -0.084967 -0.117647        0                  0   \n",
       "765                 -0.333333  0.058824        0                  0   \n",
       "766                 -0.061438  1.058824        1                  0   \n",
       "767                 -0.150327 -0.352941        0                  0   \n",
       "\n",
       "     BMIRanges_Overweight  BMIRanges_Obese  INSULIN_DESC_Normal  \\\n",
       "0                       0                1                    0   \n",
       "1                       1                0                    1   \n",
       "2                       0                0                    0   \n",
       "3                       1                0                    1   \n",
       "4                       0                1                    0   \n",
       "..                    ...              ...                  ...   \n",
       "763                     0                1                    0   \n",
       "764                     0                1                    1   \n",
       "765                     1                0                    1   \n",
       "766                     0                1                    0   \n",
       "767                     0                1                    1   \n",
       "\n",
       "     NewGlucose_Normal  NewGlucose_Secret  NewGlucose_High  \n",
       "0                    0                  0                1  \n",
       "1                    1                  0                0  \n",
       "2                    0                  0                1  \n",
       "3                    1                  0                0  \n",
       "4                    0                  0                1  \n",
       "..                 ...                ...              ...  \n",
       "763                  0                  1                0  \n",
       "764                  0                  1                0  \n",
       "765                  0                  1                0  \n",
       "766                  0                  1                0  \n",
       "767                  1                  0                0  \n",
       "\n",
       "[768 rows x 16 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:47.699709Z",
     "iopub.status.busy": "2020-08-18T01:10:47.698855Z",
     "iopub.status.idle": "2020-08-18T01:10:47.715012Z",
     "shell.execute_reply": "2020-08-18T01:10:47.714021Z"
    },
    "papermill": {
     "duration": 0.07026,
     "end_time": "2020-08-18T01:10:47.715232",
     "exception": false,
     "start_time": "2020-08-18T01:10:47.644972",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 16 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    float64\n",
      " 1   Glucose                   768 non-null    float64\n",
      " 2   BloodPressure             768 non-null    float64\n",
      " 3   SkinThickness             768 non-null    float64\n",
      " 4   Insulin                   768 non-null    float64\n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    float64\n",
      " 8   Outcome                   768 non-null    int64  \n",
      " 9   BMIRanges_Healthy         768 non-null    uint8  \n",
      " 10  BMIRanges_Overweight      768 non-null    uint8  \n",
      " 11  BMIRanges_Obese           768 non-null    uint8  \n",
      " 12  INSULIN_DESC_Normal       768 non-null    uint8  \n",
      " 13  NewGlucose_Normal         768 non-null    uint8  \n",
      " 14  NewGlucose_Secret         768 non-null    uint8  \n",
      " 15  NewGlucose_High           768 non-null    uint8  \n",
      "dtypes: float64(8), int64(1), uint8(7)\n",
      "memory usage: 59.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.047394,
     "end_time": "2020-08-18T01:10:47.813062",
     "exception": false,
     "start_time": "2020-08-18T01:10:47.765668",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:47.922722Z",
     "iopub.status.busy": "2020-08-18T01:10:47.919021Z",
     "iopub.status.idle": "2020-08-18T01:10:55.846093Z",
     "shell.execute_reply": "2020-08-18T01:10:55.846939Z"
    },
    "papermill": {
     "duration": 7.985427,
     "end_time": "2020-08-18T01:10:55.847224",
     "exception": false,
     "start_time": "2020-08-18T01:10:47.861797",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Holdout Method: LR 0.8571428571428571\n",
      "Cross Val Score LR 0.8462576896787424\n",
      "------------------------------------\n",
      "Holdout Method: KNN 0.7922077922077922\n",
      "Cross Val Score KNN 0.8268113465481888\n",
      "------------------------------------\n",
      "Holdout Method: SVR 0.8506493506493507\n",
      "Cross Val Score SVR 0.8436773752563227\n",
      "------------------------------------\n",
      "Holdout Method: CART 0.7727272727272727\n",
      "Cross Val Score CART 0.8607484620642515\n",
      "------------------------------------\n",
      "Holdout Method: RandomForests 0.8376623376623377\n",
      "Cross Val Score RandomForests 0.878879015721121\n",
      "------------------------------------\n",
      "Holdout Method: GradientBoosting 0.8636363636363636\n",
      "Cross Val Score GradientBoosting 0.8984962406015038\n",
      "------------------------------------\n",
      "Holdout Method: XGBoost 0.8441558441558441\n",
      "Cross Val Score XGBoost 0.8868079289131922\n",
      "------------------------------------\n",
      "Holdout Method: Light GBM 0.8701298701298701\n",
      "Cross Val Score Light GBM 0.8920027341079972\n",
      "------------------------------------\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(\"Outcome\",axis=1)\n",
    "y = df[\"Outcome\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=46)\n",
    "\n",
    "for name,model in models:\n",
    "    mod = model.fit(X_train,y_train) #trainleri modele fit etmek\n",
    "    y_pred = mod.predict(X_test) # tahmin\n",
    "    acc = accuracy_score(y_test, y_pred) #rmse hesabı\n",
    "    cvscore = cross_val_score(model, X,y, cv = 10).mean()\n",
    "    print(\"Holdout Method:\",end=\" \")\n",
    "    print(name,acc) #yazdırılacak kısım\n",
    "    print(\"Cross Val Score\",end=\" \")\n",
    "    print(name,cvscore)\n",
    "    print(\"------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.075917,
     "end_time": "2020-08-18T01:10:55.984007",
     "exception": false,
     "start_time": "2020-08-18T01:10:55.908090",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.047592,
     "end_time": "2020-08-18T01:10:56.089332",
     "exception": false,
     "start_time": "2020-08-18T01:10:56.041740",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# KNN Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:10:56.197699Z",
     "iopub.status.busy": "2020-08-18T01:10:56.196783Z",
     "iopub.status.idle": "2020-08-18T01:11:00.022657Z",
     "shell.execute_reply": "2020-08-18T01:11:00.021896Z"
    },
    "papermill": {
     "duration": 3.885591,
     "end_time": "2020-08-18T01:11:00.022819",
     "exception": false,
     "start_time": "2020-08-18T01:10:56.137228",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "knn_params = {\"n_neighbors\": np.arange(2,30,1)}\n",
    "\n",
    "knn_model = KNeighborsClassifier()\n",
    "\n",
    "knn_cv_model = GridSearchCV(knn_model, knn_params, cv = 10).fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:11:00.127458Z",
     "iopub.status.busy": "2020-08-18T01:11:00.126494Z",
     "iopub.status.idle": "2020-08-18T01:11:00.130650Z",
     "shell.execute_reply": "2020-08-18T01:11:00.131391Z"
    },
    "papermill": {
     "duration": 0.059427,
     "end_time": "2020-08-18T01:11:00.131566",
     "exception": false,
     "start_time": "2020-08-18T01:11:00.072139",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 27}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:11:00.237422Z",
     "iopub.status.busy": "2020-08-18T01:11:00.236558Z",
     "iopub.status.idle": "2020-08-18T01:11:00.241601Z",
     "shell.execute_reply": "2020-08-18T01:11:00.242188Z"
    },
    "papermill": {
     "duration": 0.062376,
     "end_time": "2020-08-18T01:11:00.242367",
     "exception": false,
     "start_time": "2020-08-18T01:11:00.179991",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "knn_tuned = KNeighborsClassifier(**knn_cv_model.best_params_).fit(X,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:11:00.356629Z",
     "iopub.status.busy": "2020-08-18T01:11:00.355745Z",
     "iopub.status.idle": "2020-08-18T01:11:00.520496Z",
     "shell.execute_reply": "2020-08-18T01:11:00.519509Z"
    },
    "papermill": {
     "duration": 0.229623,
     "end_time": "2020-08-18T01:11:00.520673",
     "exception": false,
     "start_time": "2020-08-18T01:11:00.291050",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8489747095010254"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(knn_tuned, X,y, cv = 10).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.048129,
     "end_time": "2020-08-18T01:11:00.618201",
     "exception": false,
     "start_time": "2020-08-18T01:11:00.570072",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# CART Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:11:00.723230Z",
     "iopub.status.busy": "2020-08-18T01:11:00.722009Z",
     "iopub.status.idle": "2020-08-18T01:11:00.725830Z",
     "shell.execute_reply": "2020-08-18T01:11:00.725037Z"
    },
    "papermill": {
     "duration": 0.059167,
     "end_time": "2020-08-18T01:11:00.725977",
     "exception": false,
     "start_time": "2020-08-18T01:11:00.666810",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cart_model = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:11:00.830502Z",
     "iopub.status.busy": "2020-08-18T01:11:00.829535Z",
     "iopub.status.idle": "2020-08-18T01:11:00.832998Z",
     "shell.execute_reply": "2020-08-18T01:11:00.832249Z"
    },
    "papermill": {
     "duration": 0.058227,
     "end_time": "2020-08-18T01:11:00.833146",
     "exception": false,
     "start_time": "2020-08-18T01:11:00.774919",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cart_params = {\"max_depth\": [2,3,4,5,10,20,100, 1000],\n",
    "              \"min_samples_split\": [2,10,5,30,50,10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:11:00.938510Z",
     "iopub.status.busy": "2020-08-18T01:11:00.937571Z",
     "iopub.status.idle": "2020-08-18T01:11:04.666213Z",
     "shell.execute_reply": "2020-08-18T01:11:04.665449Z"
    },
    "papermill": {
     "duration": 3.785095,
     "end_time": "2020-08-18T01:11:04.666357",
     "exception": false,
     "start_time": "2020-08-18T01:11:00.881262",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 48 candidates, totalling 480 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  38 tasks      | elapsed:    1.8s\n",
      "[Parallel(n_jobs=-1)]: Done 480 out of 480 | elapsed:    3.7s finished\n"
     ]
    }
   ],
   "source": [
    "cart_cv_model = GridSearchCV(cart_model, cart_params, cv = 10, n_jobs = -1, verbose =  2).fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:11:04.772833Z",
     "iopub.status.busy": "2020-08-18T01:11:04.771542Z",
     "iopub.status.idle": "2020-08-18T01:11:04.776299Z",
     "shell.execute_reply": "2020-08-18T01:11:04.775695Z"
    },
    "papermill": {
     "duration": 0.060355,
     "end_time": "2020-08-18T01:11:04.776440",
     "exception": false,
     "start_time": "2020-08-18T01:11:04.716085",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 4, 'min_samples_split': 30}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cart_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:11:04.884190Z",
     "iopub.status.busy": "2020-08-18T01:11:04.883057Z",
     "iopub.status.idle": "2020-08-18T01:11:04.890179Z",
     "shell.execute_reply": "2020-08-18T01:11:04.889542Z"
    },
    "papermill": {
     "duration": 0.064857,
     "end_time": "2020-08-18T01:11:04.890320",
     "exception": false,
     "start_time": "2020-08-18T01:11:04.825463",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cart_tuned = DecisionTreeClassifier(**cart_cv_model.best_params_).fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:11:04.995639Z",
     "iopub.status.busy": "2020-08-18T01:11:04.994697Z",
     "iopub.status.idle": "2020-08-18T01:11:05.094279Z",
     "shell.execute_reply": "2020-08-18T01:11:05.093561Z"
    },
    "papermill": {
     "duration": 0.154723,
     "end_time": "2020-08-18T01:11:05.094422",
     "exception": false,
     "start_time": "2020-08-18T01:11:04.939699",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.876332877648667"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(cart_tuned, X,y, cv = 10).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.049621,
     "end_time": "2020-08-18T01:11:05.194494",
     "exception": false,
     "start_time": "2020-08-18T01:11:05.144873",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# RF Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:11:05.303216Z",
     "iopub.status.busy": "2020-08-18T01:11:05.302023Z",
     "iopub.status.idle": "2020-08-18T01:11:05.305685Z",
     "shell.execute_reply": "2020-08-18T01:11:05.304901Z"
    },
    "papermill": {
     "duration": 0.059977,
     "end_time": "2020-08-18T01:11:05.305824",
     "exception": false,
     "start_time": "2020-08-18T01:11:05.245847",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rf_params = {\"max_depth\": [5,10,None],\n",
    "            \"max_features\": [2,5,10],\n",
    "            \"n_estimators\": [100, 500, 900],\n",
    "            \"min_samples_split\": [2,10,30]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:11:05.412917Z",
     "iopub.status.busy": "2020-08-18T01:11:05.411805Z",
     "iopub.status.idle": "2020-08-18T01:11:05.415518Z",
     "shell.execute_reply": "2020-08-18T01:11:05.414688Z"
    },
    "papermill": {
     "duration": 0.06039,
     "end_time": "2020-08-18T01:11:05.415657",
     "exception": false,
     "start_time": "2020-08-18T01:11:05.355267",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rf_model = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:11:05.523739Z",
     "iopub.status.busy": "2020-08-18T01:11:05.522516Z",
     "iopub.status.idle": "2020-08-18T01:18:32.181355Z",
     "shell.execute_reply": "2020-08-18T01:18:32.181990Z"
    },
    "papermill": {
     "duration": 446.716694,
     "end_time": "2020-08-18T01:18:32.182473",
     "exception": false,
     "start_time": "2020-08-18T01:11:05.465779",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 81 candidates, totalling 810 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   14.1s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:  3.1min\n",
      "[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:  5.6min\n",
      "[Parallel(n_jobs=-1)]: Done 810 out of 810 | elapsed:  7.4min finished\n"
     ]
    }
   ],
   "source": [
    "rf_cv_model = GridSearchCV(rf_model, rf_params, cv = 10, n_jobs = -1, verbose = 2).fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:18:32.293729Z",
     "iopub.status.busy": "2020-08-18T01:18:32.292513Z",
     "iopub.status.idle": "2020-08-18T01:18:32.297446Z",
     "shell.execute_reply": "2020-08-18T01:18:32.296662Z"
    },
    "papermill": {
     "duration": 0.063539,
     "end_time": "2020-08-18T01:18:32.297583",
     "exception": false,
     "start_time": "2020-08-18T01:18:32.234044",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 10,\n",
       " 'max_features': 10,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 900}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:18:32.410325Z",
     "iopub.status.busy": "2020-08-18T01:18:32.409373Z",
     "iopub.status.idle": "2020-08-18T01:18:35.588337Z",
     "shell.execute_reply": "2020-08-18T01:18:35.587578Z"
    },
    "papermill": {
     "duration": 3.240005,
     "end_time": "2020-08-18T01:18:35.588495",
     "exception": false,
     "start_time": "2020-08-18T01:18:32.348490",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rf_tuned = RandomForestClassifier(**rf_cv_model.best_params_).fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:18:35.701286Z",
     "iopub.status.busy": "2020-08-18T01:18:35.700444Z",
     "iopub.status.idle": "2020-08-18T01:19:06.917493Z",
     "shell.execute_reply": "2020-08-18T01:19:06.918139Z"
    },
    "papermill": {
     "duration": 31.277931,
     "end_time": "2020-08-18T01:19:06.918336",
     "exception": false,
     "start_time": "2020-08-18T01:18:35.640405",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8893711551606287"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(rf_tuned, X,y, cv = 10).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.05006,
     "end_time": "2020-08-18T01:19:07.019392",
     "exception": false,
     "start_time": "2020-08-18T01:19:06.969332",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# SVC Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:19:07.127444Z",
     "iopub.status.busy": "2020-08-18T01:19:07.126441Z",
     "iopub.status.idle": "2020-08-18T01:19:07.128990Z",
     "shell.execute_reply": "2020-08-18T01:19:07.129568Z"
    },
    "papermill": {
     "duration": 0.05954,
     "end_time": "2020-08-18T01:19:07.129745",
     "exception": false,
     "start_time": "2020-08-18T01:19:07.070205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "svc_model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:19:07.252656Z",
     "iopub.status.busy": "2020-08-18T01:19:07.251503Z",
     "iopub.status.idle": "2020-08-18T01:19:08.766396Z",
     "shell.execute_reply": "2020-08-18T01:19:08.767173Z"
    },
    "papermill": {
     "duration": 1.584569,
     "end_time": "2020-08-18T01:19:08.767402",
     "exception": false,
     "start_time": "2020-08-18T01:19:07.182833",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 12 candidates, totalling 120 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  92 tasks      | elapsed:    1.1s\n",
      "[Parallel(n_jobs=-1)]: Done 120 out of 120 | elapsed:    1.5s finished\n"
     ]
    }
   ],
   "source": [
    "svc_params = {\"C\": [0.01,0.001, 0.2, 0.1,0.5,0.8,0.9,1, 10, 100, 500,1000]}\n",
    "\n",
    "svc_cv_model = GridSearchCV(svc_model, svc_params, cv = 10, n_jobs = -1, verbose =  2).fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:19:08.878330Z",
     "iopub.status.busy": "2020-08-18T01:19:08.877083Z",
     "iopub.status.idle": "2020-08-18T01:19:08.881955Z",
     "shell.execute_reply": "2020-08-18T01:19:08.881295Z"
    },
    "papermill": {
     "duration": 0.062261,
     "end_time": "2020-08-18T01:19:08.882091",
     "exception": false,
     "start_time": "2020-08-18T01:19:08.819830",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:19:08.993971Z",
     "iopub.status.busy": "2020-08-18T01:19:08.993188Z",
     "iopub.status.idle": "2020-08-18T01:19:09.016722Z",
     "shell.execute_reply": "2020-08-18T01:19:09.016016Z"
    },
    "papermill": {
     "duration": 0.083408,
     "end_time": "2020-08-18T01:19:09.016870",
     "exception": false,
     "start_time": "2020-08-18T01:19:08.933462",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "svc_tuned = SVC(**svc_cv_model.best_params_).fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:19:09.129855Z",
     "iopub.status.busy": "2020-08-18T01:19:09.128996Z",
     "iopub.status.idle": "2020-08-18T01:19:09.370501Z",
     "shell.execute_reply": "2020-08-18T01:19:09.369861Z"
    },
    "papermill": {
     "duration": 0.300564,
     "end_time": "2020-08-18T01:19:09.370647",
     "exception": false,
     "start_time": "2020-08-18T01:19:09.070083",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8436773752563227"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(svc_tuned, X,y, cv = 10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.051066,
     "end_time": "2020-08-18T01:19:09.473404",
     "exception": false,
     "start_time": "2020-08-18T01:19:09.422338",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.051058,
     "end_time": "2020-08-18T01:19:09.576046",
     "exception": false,
     "start_time": "2020-08-18T01:19:09.524988",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# GB Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:19:09.685671Z",
     "iopub.status.busy": "2020-08-18T01:19:09.684897Z",
     "iopub.status.idle": "2020-08-18T01:19:09.688621Z",
     "shell.execute_reply": "2020-08-18T01:19:09.687840Z"
    },
    "papermill": {
     "duration": 0.060487,
     "end_time": "2020-08-18T01:19:09.688779",
     "exception": false,
     "start_time": "2020-08-18T01:19:09.628292",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "gb_model = GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:19:09.800135Z",
     "iopub.status.busy": "2020-08-18T01:19:09.799317Z",
     "iopub.status.idle": "2020-08-18T01:19:09.803081Z",
     "shell.execute_reply": "2020-08-18T01:19:09.802314Z"
    },
    "papermill": {
     "duration": 0.062299,
     "end_time": "2020-08-18T01:19:09.803240",
     "exception": false,
     "start_time": "2020-08-18T01:19:09.740941",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "gbm_params = {\"learning_rate\": [0.001,0.1,0.01],\n",
    "             \"max_depth\": [3,5,8,10],\n",
    "             \"n_estimators\": [200,500,1000],\n",
    "             \"subsample\": [1,0.5,0.8]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:19:09.916824Z",
     "iopub.status.busy": "2020-08-18T01:19:09.915892Z",
     "iopub.status.idle": "2020-08-18T01:39:09.090686Z",
     "shell.execute_reply": "2020-08-18T01:39:09.089578Z"
    },
    "papermill": {
     "duration": 1199.234763,
     "end_time": "2020-08-18T01:39:09.090938",
     "exception": false,
     "start_time": "2020-08-18T01:19:09.856175",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    5.9s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:  6.9min\n",
      "[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed: 10.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1005 tasks      | elapsed: 17.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1080 out of 1080 | elapsed: 20.0min finished\n"
     ]
    }
   ],
   "source": [
    "gbm_cv_model = GridSearchCV(gb_model, \n",
    "                            gbm_params, \n",
    "                            cv = 10, \n",
    "                            n_jobs=-1, \n",
    "                            verbose = 2).fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:39:09.206027Z",
     "iopub.status.busy": "2020-08-18T01:39:09.204865Z",
     "iopub.status.idle": "2020-08-18T01:39:09.209313Z",
     "shell.execute_reply": "2020-08-18T01:39:09.209917Z"
    },
    "papermill": {
     "duration": 0.06621,
     "end_time": "2020-08-18T01:39:09.210096",
     "exception": false,
     "start_time": "2020-08-18T01:39:09.143886",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 500, 'subsample': 1}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_cv_model.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:39:09.325343Z",
     "iopub.status.busy": "2020-08-18T01:39:09.324416Z",
     "iopub.status.idle": "2020-08-18T01:39:11.206775Z",
     "shell.execute_reply": "2020-08-18T01:39:11.207449Z"
    },
    "papermill": {
     "duration": 1.944596,
     "end_time": "2020-08-18T01:39:11.207630",
     "exception": false,
     "start_time": "2020-08-18T01:39:09.263034",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "gbm_tuned = GradientBoostingClassifier(**gbm_cv_model.best_params_).fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:39:11.323591Z",
     "iopub.status.busy": "2020-08-18T01:39:11.322732Z",
     "iopub.status.idle": "2020-08-18T01:39:28.822086Z",
     "shell.execute_reply": "2020-08-18T01:39:28.821341Z"
    },
    "papermill": {
     "duration": 17.560737,
     "end_time": "2020-08-18T01:39:28.822238",
     "exception": false,
     "start_time": "2020-08-18T01:39:11.261501",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.903691045796309"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(gbm_tuned, X,y, cv = 10).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.052774,
     "end_time": "2020-08-18T01:39:28.928411",
     "exception": false,
     "start_time": "2020-08-18T01:39:28.875637",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Light GBM Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:39:29.042347Z",
     "iopub.status.busy": "2020-08-18T01:39:29.041194Z",
     "iopub.status.idle": "2020-08-18T01:39:29.044681Z",
     "shell.execute_reply": "2020-08-18T01:39:29.043922Z"
    },
    "papermill": {
     "duration": 0.063543,
     "end_time": "2020-08-18T01:39:29.044813",
     "exception": false,
     "start_time": "2020-08-18T01:39:28.981270",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lgbm_params = {\"learning_rate\": [0.01, 0.05, 0.1],\n",
    "              \"n_estimators\": [200, 500, 1000],\n",
    "              \"max_depth\":[5,8,10],\n",
    "              \"colsample_bytree\": [1,0.5,0.3]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:39:29.156930Z",
     "iopub.status.busy": "2020-08-18T01:39:29.156097Z",
     "iopub.status.idle": "2020-08-18T01:39:29.159143Z",
     "shell.execute_reply": "2020-08-18T01:39:29.159725Z"
    },
    "papermill": {
     "duration": 0.062047,
     "end_time": "2020-08-18T01:39:29.159912",
     "exception": false,
     "start_time": "2020-08-18T01:39:29.097865",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lgbm_model = LGBMClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:39:29.275124Z",
     "iopub.status.busy": "2020-08-18T01:39:29.274164Z",
     "iopub.status.idle": "2020-08-18T01:41:03.718623Z",
     "shell.execute_reply": "2020-08-18T01:41:03.719379Z"
    },
    "papermill": {
     "duration": 94.505702,
     "end_time": "2020-08-18T01:41:03.719612",
     "exception": false,
     "start_time": "2020-08-18T01:39:29.213910",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 81 candidates, totalling 810 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    3.8s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   21.9s\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:   47.4s\n",
      "[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 810 out of 810 | elapsed:  1.6min finished\n"
     ]
    }
   ],
   "source": [
    "lgbm_cv_model = GridSearchCV(lgbm_model, \n",
    "                     lgbm_params, \n",
    "                     cv = 10, \n",
    "                     n_jobs = -1, \n",
    "                     verbose = 2).fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:41:03.857215Z",
     "iopub.status.busy": "2020-08-18T01:41:03.856035Z",
     "iopub.status.idle": "2020-08-18T01:41:03.861425Z",
     "shell.execute_reply": "2020-08-18T01:41:03.860655Z"
    },
    "papermill": {
     "duration": 0.072593,
     "end_time": "2020-08-18T01:41:03.861564",
     "exception": false,
     "start_time": "2020-08-18T01:41:03.788971",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 1,\n",
       " 'learning_rate': 0.05,\n",
       " 'max_depth': 5,\n",
       " 'n_estimators': 500}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbm_cv_model.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:41:03.975438Z",
     "iopub.status.busy": "2020-08-18T01:41:03.974436Z",
     "iopub.status.idle": "2020-08-18T01:41:04.161414Z",
     "shell.execute_reply": "2020-08-18T01:41:04.162191Z"
    },
    "papermill": {
     "duration": 0.247381,
     "end_time": "2020-08-18T01:41:04.162407",
     "exception": false,
     "start_time": "2020-08-18T01:41:03.915026",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lgbm_tuned = LGBMClassifier(**lgbm_cv_model.best_params_).fit(X,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:41:04.311897Z",
     "iopub.status.busy": "2020-08-18T01:41:04.310979Z",
     "iopub.status.idle": "2020-08-18T01:41:06.179894Z",
     "shell.execute_reply": "2020-08-18T01:41:06.180736Z"
    },
    "papermill": {
     "duration": 1.952694,
     "end_time": "2020-08-18T01:41:06.180945",
     "exception": false,
     "start_time": "2020-08-18T01:41:04.228251",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8959159261790839"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(lgbm_tuned, X,y, cv = 10).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.069845,
     "end_time": "2020-08-18T01:41:06.319522",
     "exception": false,
     "start_time": "2020-08-18T01:41:06.249677",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# XGB Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:41:06.443974Z",
     "iopub.status.busy": "2020-08-18T01:41:06.442846Z",
     "iopub.status.idle": "2020-08-18T01:41:06.445955Z",
     "shell.execute_reply": "2020-08-18T01:41:06.445358Z"
    },
    "papermill": {
     "duration": 0.064086,
     "end_time": "2020-08-18T01:41:06.446091",
     "exception": false,
     "start_time": "2020-08-18T01:41:06.382005",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgb_model = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:41:06.661547Z",
     "iopub.status.busy": "2020-08-18T01:41:06.660315Z",
     "iopub.status.idle": "2020-08-18T01:41:06.667186Z",
     "shell.execute_reply": "2020-08-18T01:41:06.668065Z"
    },
    "papermill": {
     "duration": 0.167564,
     "end_time": "2020-08-18T01:41:06.668296",
     "exception": false,
     "start_time": "2020-08-18T01:41:06.500732",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgb_params = {\"learning_rate\": [0.1,0.01,1],\n",
    "             \"max_depth\": [2,5,8],\n",
    "             \"n_estimators\": [100,500,1000],\n",
    "             \"colsample_bytree\": [0.3,0.6,1]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:41:06.803147Z",
     "iopub.status.busy": "2020-08-18T01:41:06.801996Z",
     "iopub.status.idle": "2020-08-18T01:42:38.980711Z",
     "shell.execute_reply": "2020-08-18T01:42:38.981479Z"
    },
    "papermill": {
     "duration": 92.250451,
     "end_time": "2020-08-18T01:42:38.981719",
     "exception": false,
     "start_time": "2020-08-18T01:41:06.731268",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 81 candidates, totalling 810 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    3.0s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   13.8s\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:   33.7s\n",
      "[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 810 out of 810 | elapsed:  1.5min finished\n"
     ]
    }
   ],
   "source": [
    "xgb_cv_model  = GridSearchCV(xgb_model,xgb_params, cv = 10, n_jobs = -1, verbose = 2).fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:42:39.121846Z",
     "iopub.status.busy": "2020-08-18T01:42:39.120949Z",
     "iopub.status.idle": "2020-08-18T01:42:39.124314Z",
     "shell.execute_reply": "2020-08-18T01:42:39.124977Z"
    },
    "papermill": {
     "duration": 0.077374,
     "end_time": "2020-08-18T01:42:39.125186",
     "exception": false,
     "start_time": "2020-08-18T01:42:39.047812",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 0.6,\n",
       " 'learning_rate': 0.01,\n",
       " 'max_depth': 2,\n",
       " 'n_estimators': 1000}"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_cv_model.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:42:39.249670Z",
     "iopub.status.busy": "2020-08-18T01:42:39.247935Z",
     "iopub.status.idle": "2020-08-18T01:42:39.470145Z",
     "shell.execute_reply": "2020-08-18T01:42:39.470848Z"
    },
    "papermill": {
     "duration": 0.28446,
     "end_time": "2020-08-18T01:42:39.471080",
     "exception": false,
     "start_time": "2020-08-18T01:42:39.186620",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgb_tuned = XGBClassifier(**xgb_cv_model.best_params_).fit(X,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-18T01:42:39.611641Z",
     "iopub.status.busy": "2020-08-18T01:42:39.610502Z",
     "iopub.status.idle": "2020-08-18T01:42:41.899665Z",
     "shell.execute_reply": "2020-08-18T01:42:41.900461Z"
    },
    "papermill": {
     "duration": 2.363097,
     "end_time": "2020-08-18T01:42:41.900662",
     "exception": false,
     "start_time": "2020-08-18T01:42:39.537565",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9036568694463429"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(xgb_tuned, X,y, cv = 10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.066824,
     "end_time": "2020-08-18T01:42:42.033881",
     "exception": false,
     "start_time": "2020-08-18T01:42:41.967057",
     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": []
  }
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