Main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\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, GradientBoostingClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore')\n",
    "sns.set()\n",
    "plt.style.use(\"ggplot\")\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the dataset from dir\n",
    "df = pd.read_csv(\"diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Outcome.value_counts()*100/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Outcome'].value_counts()*100/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "plt.xlabel('Age', fontsize=10)\n",
    "plt.ylabel('Count', fontsize=10)\n",
    "df['Age'].hist(edgecolor=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"MAX AGE: \"+str(df['Age'].max()))\n",
    "print(\"MIN AGE: \"+str(df['Age'].min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(4,2, figsize=(20,20))\n",
    "sns.distplot(df.Pregnancies, bins=20, ax=ax[0,0], color=\"red\")\n",
    "sns.distplot(df.Glucose, bins=20, ax=ax[0,1], color=\"red\")\n",
    "sns.distplot(df.BloodPressure, bins=20, ax=ax[1,0], color=\"red\")\n",
    "sns.distplot(df.SkinThickness, bins=20, ax=ax[1,1], color=\"red\")\n",
    "sns.distplot(df.Insulin, bins=20, ax=ax[2,0], color=\"red\")\n",
    "sns.distplot(df.BMI, bins=20, ax=ax[2,1], color=\"red\")\n",
    "sns.distplot(df.DiabetesPedigreeFunction, bins=20, ax=ax[3,0], color=\"red\")\n",
    "sns.distplot(df.Age, bins=20, ax=ax[3,1], color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby(\"Outcome\").agg({'Pregnancies':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby(\"Outcome\").agg({'Pregnancies':'max'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby(\"Outcome\").agg({'Glucose':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby(\"Outcome\").agg({'Glucose':'max'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f,ax = plt.subplots(1,2, figsize=(18,8))\n",
    "df['Outcome'].value_counts().plot.pie(explode=[0,0.1],autopct = \"%1.1f%%\", ax=ax[0], shadow=True)\n",
    "ax[0].set_title('target')\n",
    "ax[0].set_ylabel('')\n",
    "sns.countplot('Outcome', data=df, ax=ax[1])\n",
    "ax[1].set_title('Outcome')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f,ax = plt.subplots(figsize=[20,15])\n",
    "sns.heatmap(df.corr(), annot=True, fmt = '.2f', ax=ax, cmap='magma')\n",
    "ax.set_title(\"Correlation Matrix\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
    "       'BMI', 'DiabetesPedigreeFunction', 'Age']] = df[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
    "       'BMI', 'DiabetesPedigreeFunction', 'Age']].replace(0, np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import missingno as msno\n",
    "msno.bar(df, color=\"orange\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def median_target(var):   \n",
    "    temp = df[df[var].notnull()]\n",
    "    temp = temp[[var, 'Outcome']].groupby(['Outcome'])[[var]].median().reset_index()\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = df.columns\n",
    "columns = columns.drop(\"Outcome\")\n",
    "for i in columns:\n",
    "    median_target(i)\n",
    "    df.loc[(df['Outcome'] == 0 ) & (df[i].isnull()), i] = median_target(i)[i][0]\n",
    "    df.loc[(df['Outcome'] == 1 ) & (df[i].isnull()), i] = median_target(i)[i][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = sns.pairplot(df, hue=\"Outcome\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in df:\n",
    "    Q1 = df[feature].quantile(0.25)\n",
    "    Q3 = df[feature].quantile(0.75)\n",
    "    IQR = Q3-Q1\n",
    "    lower = Q1-1.5*IQR\n",
    "    upper = Q3+1.5*IQR\n",
    "    if df[(df[feature]>upper)].any(axis=None):\n",
    "        print(feature, \"yes\")\n",
    "    else:\n",
    "        print(feature, \"no\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "sns.boxplot(x= df[\"Insulin\"], color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = df.Insulin.quantile(0.25)\n",
    "Q3 = df.Insulin.quantile(0.75)\n",
    "IQR = Q3-Q1\n",
    "lower = Q1-1.5*IQR\n",
    "upper = Q3+1.5*IQR\n",
    "df.loc[df['Insulin']>upper, \"Insulin\"] = upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "sns.boxplot(x= df[\"Insulin\"], color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "sns.boxplot(x= df[\"Pregnancies\"], color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores = lof.negative_outlier_factor_\n",
    "np.sort(df_scores)[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresold = np.sort(df_scores)[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier = df_scores>thresold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[outlier]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "sns.boxplot(x= df[\"Pregnancies\"], color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NewBMI = pd.Series([\"Underweight\",\"Normal\", \"Overweight\",\"Obesity 1\", \"Obesity 2\", \"Obesity 3\"], dtype = \"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NewBMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['NewBMI'] = NewBMI\n",
    "df.loc[df[\"BMI\"]<18.5, \"NewBMI\"] = NewBMI[0]\n",
    "df.loc[(df[\"BMI\"]>18.5) & df[\"BMI\"]<=24.9, \"NewBMI\"] = NewBMI[1]\n",
    "df.loc[(df[\"BMI\"]>24.9) & df[\"BMI\"]<=29.9, \"NewBMI\"] = NewBMI[2]\n",
    "df.loc[(df[\"BMI\"]>29.9) & df[\"BMI\"]<=34.9, \"NewBMI\"] = NewBMI[3]\n",
    "df.loc[(df[\"BMI\"]>34.9) & df[\"BMI\"]<=39.9, \"NewBMI\"] = NewBMI[4]\n",
    "df.loc[df[\"BMI\"]>39.9, \"NewBMI\"] = NewBMI[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_insuline(row):\n",
    "    if row[\"Insulin\"]>=16 and row[\"Insulin\"]<=166:\n",
    "        return \"Normal\"\n",
    "    else:\n",
    "        return \"Abnormal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.assign(NewInsulinScore=df.apply(set_insuline, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NewGlucose = pd.Series([\"Low\", \"Normal\", \"Overweight\", \"Secret\", \"High\"], dtype = \"category\")\n",
    "df[\"NewGlucose\"] = NewGlucose\n",
    "df.loc[df[\"Glucose\"] <= 70, \"NewGlucose\"] = NewGlucose[0]\n",
    "df.loc[(df[\"Glucose\"] > 70) & (df[\"Glucose\"] <= 99), \"NewGlucose\"] = NewGlucose[1]\n",
    "df.loc[(df[\"Glucose\"] > 99) & (df[\"Glucose\"] <= 126), \"NewGlucose\"] = NewGlucose[2]\n",
    "df.loc[df[\"Glucose\"] > 126 ,\"NewGlucose\"] = NewGlucose[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns = [\"NewBMI\", \"NewInsulinScore\", \"NewGlucose\"], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_df = df[['NewBMI_Obesity 1',\n",
    "       'NewBMI_Obesity 2', 'NewBMI_Obesity 3', 'NewBMI_Overweight',\n",
    "       'NewBMI_Underweight', 'NewInsulinScore_Normal', 'NewGlucose_Low',\n",
    "       'NewGlucose_Normal', 'NewGlucose_Overweight', 'NewGlucose_Secret']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Outcome']\n",
    "X=df.drop(['Outcome','NewBMI_Obesity 1',\n",
    "       'NewBMI_Obesity 2', 'NewBMI_Obesity 3', 'NewBMI_Overweight',\n",
    "       'NewBMI_Underweight', 'NewInsulinScore_Normal', 'NewGlucose_Low',\n",
    "       'NewGlucose_Normal', 'NewGlucose_Overweight', 'NewGlucose_Secret'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = X.columns\n",
    "index = X.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "transformer = RobustScaler().fit(X)\n",
    "X=transformer.transform(X)\n",
    "X=pd.DataFrame(X, columns = cols, index = index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.177778</td>\n",
       "      <td>0.669707</td>\n",
       "      <td>1.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.600000</td>\n",
       "      <td>-0.049511</td>\n",
       "      <td>0.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.650</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.966667</td>\n",
       "      <td>0.786971</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.700</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>-0.714286</td>\n",
       "      <td>-0.126866</td>\n",
       "      <td>-0.433333</td>\n",
       "      <td>-0.528990</td>\n",
       "      <td>-0.470588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977612</td>\n",
       "      <td>1.233333</td>\n",
       "      <td>4.998046</td>\n",
       "      <td>0.235294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "0         0.75    0.775          0.000       1.000000  1.000000  0.177778   \n",
       "1        -0.50   -0.800         -0.375       0.142857  0.000000 -0.600000   \n",
       "2         1.25    1.650         -0.500       0.571429  1.000000 -0.966667   \n",
       "3        -0.50   -0.700         -0.375      -0.714286 -0.126866 -0.433333   \n",
       "4         0.50    0.500         -2.000       1.000000  0.977612  1.233333   \n",
       "\n",
       "   DiabetesPedigreeFunction       Age  \n",
       "0                  0.669707  1.235294  \n",
       "1                 -0.049511  0.117647  \n",
       "2                  0.786971  0.176471  \n",
       "3                 -0.528990 -0.470588  \n",
       "4                  4.998046  0.235294  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([X, categorical_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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>NewBMI_Obesity 1</th>\n",
       "      <th>NewBMI_Obesity 2</th>\n",
       "      <th>NewBMI_Obesity 3</th>\n",
       "      <th>NewBMI_Overweight</th>\n",
       "      <th>NewBMI_Underweight</th>\n",
       "      <th>NewInsulinScore_Normal</th>\n",
       "      <th>NewGlucose_Low</th>\n",
       "      <th>NewGlucose_Normal</th>\n",
       "      <th>NewGlucose_Overweight</th>\n",
       "      <th>NewGlucose_Secret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.177778</td>\n",
       "      <td>0.669707</td>\n",
       "      <td>1.235294</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.600000</td>\n",
       "      <td>-0.049511</td>\n",
       "      <td>0.117647</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.650</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.966667</td>\n",
       "      <td>0.786971</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.700</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>-0.714286</td>\n",
       "      <td>-0.126866</td>\n",
       "      <td>-0.433333</td>\n",
       "      <td>-0.528990</td>\n",
       "      <td>-0.470588</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977612</td>\n",
       "      <td>1.233333</td>\n",
       "      <td>4.998046</td>\n",
       "      <td>0.235294</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "0         0.75    0.775          0.000       1.000000  1.000000  0.177778   \n",
       "1        -0.50   -0.800         -0.375       0.142857  0.000000 -0.600000   \n",
       "2         1.25    1.650         -0.500       0.571429  1.000000 -0.966667   \n",
       "3        -0.50   -0.700         -0.375      -0.714286 -0.126866 -0.433333   \n",
       "4         0.50    0.500         -2.000       1.000000  0.977612  1.233333   \n",
       "\n",
       "   DiabetesPedigreeFunction       Age  NewBMI_Obesity 1  NewBMI_Obesity 2  \\\n",
       "0                  0.669707  1.235294                 0                 1   \n",
       "1                 -0.049511  0.117647                 0                 1   \n",
       "2                  0.786971  0.176471                 0                 1   \n",
       "3                 -0.528990 -0.470588                 0                 1   \n",
       "4                  4.998046  0.235294                 0                 0   \n",
       "\n",
       "   NewBMI_Obesity 3  NewBMI_Overweight  NewBMI_Underweight  \\\n",
       "0                 0                  0                   0   \n",
       "1                 0                  0                   0   \n",
       "2                 0                  0                   0   \n",
       "3                 0                  0                   0   \n",
       "4                 1                  0                   0   \n",
       "\n",
       "   NewInsulinScore_Normal  NewGlucose_Low  NewGlucose_Normal  \\\n",
       "0                       0               0                  0   \n",
       "1                       1               0                  1   \n",
       "2                       0               0                  0   \n",
       "3                       1               0                  1   \n",
       "4                       0               0                  0   \n",
       "\n",
       "   NewGlucose_Overweight  NewGlucose_Secret  \n",
       "0                      0                  1  \n",
       "1                      0                  0  \n",
       "2                      0                  1  \n",
       "3                      0                  0  \n",
       "4                      0                  1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train , y_test = train_test_split(X,y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler =StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Machine Learning Algo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regreesion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KNN\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred = knn.predict(X_test)\n",
    "print(accuracy_score(y_train, knn.predict(X_train)))\n",
    "knn_acc = accuracy_score(y_test, knn.predict(X_test))\n",
    "print(accuracy_score(y_test, knn.predict(X_test)))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))\n",
    "# SVM\n",
    "svc = SVC(probability=True)\n",
    "parameter = {\n",
    "    \"gamma\":[0.0001, 0.001, 0.01, 0.1],\n",
    "    'C': [0.01, 0.05,0.5, 0.01, 1, 10, 15, 20]\n",
    "}\n",
    "grid_search = GridSearchCV(svc, parameter)\n",
    "grid_search.fit(X_train, y_train)\n",
    "# best_parameter\n",
    "grid_search.best_params_\n",
    "grid_search.best_score_\n",
    "svc = SVC(C=10, gamma = 0.01, probability=True)\n",
    "svc.fit(X_train, y_train)\n",
    "y_pred = svc.predict(X_test)\n",
    "print(accuracy_score(y_train, svc.predict(X_train)))\n",
    "svc_acc = accuracy_score(y_test, svc.predict(X_test))\n",
    "print(accuracy_score(y_test, svc.predict(X_test)))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8797435897435897"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_search_dt.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=15, max_features=0.75,\n",
       "                       min_samples_leaf=2, min_samples_split=3,\n",
       "                       n_estimators=130)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=15, max_features=0.75,\n",
       "                       min_samples_leaf=2, min_samples_split=3,\n",
       "                       n_estimators=130)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=15, max_features=0.75,\n",
       "                       min_samples_leaf=2, min_samples_split=3,\n",
       "                       n_estimators=130)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rand_clf = RandomForestClassifier(criterion = 'entropy', max_depth = 15, max_features = 0.75, min_samples_leaf = 2, min_samples_split = 3, n_estimators = 130)\n",
    "rand_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rand_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9950657894736842\n",
      "0.8947368421052632\n",
      "[[88 10]\n",
      " [ 6 48]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.90      0.92        98\n",
      "           1       0.83      0.89      0.86        54\n",
      "\n",
      "    accuracy                           0.89       152\n",
      "   macro avg       0.88      0.89      0.89       152\n",
      "weighted avg       0.90      0.89      0.90       152\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = rand_clf.predict(X_test)\n",
    "print(accuracy_score(y_train, rand_clf.predict(X_train)))\n",
    "rand_acc = accuracy_score(y_test, rand_clf.predict(X_test))\n",
    "print(accuracy_score(y_test, rand_clf.predict(X_test)))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.001, 0.1, 1, 10],\n",
       "                         &#x27;loss&#x27;: [&#x27;deviance&#x27;, &#x27;exponential&#x27;],\n",
       "                         &#x27;n_estimators&#x27;: [100, 150, 180, 200]},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.001, 0.1, 1, 10],\n",
       "                         &#x27;loss&#x27;: [&#x27;deviance&#x27;, &#x27;exponential&#x27;],\n",
       "                         &#x27;n_estimators&#x27;: [100, 150, 180, 200]},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
       "             param_grid={'learning_rate': [0.001, 0.1, 1, 10],\n",
       "                         'loss': ['deviance', 'exponential'],\n",
       "                         'n_estimators': [100, 150, 180, 200]},\n",
       "             verbose=1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier()\n",
    "\n",
    "parameters = {\n",
    "    'loss': ['deviance', 'exponential'],\n",
    "    'learning_rate': [0.001, 0.1, 1, 10],\n",
    "    'n_estimators': [100, 150, 180, 200]\n",
    "}\n",
    "\n",
    "grid_search_gbc = GridSearchCV(gbc, parameters, cv = 10, n_jobs = -1, verbose = 1)\n",
    "grid_search_gbc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1, 'loss': 'exponential', 'n_estimators': 150}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_search_gbc.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.888032786885246"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_search_gbc.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(loss=&#x27;exponential&#x27;, n_estimators=150)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(loss=&#x27;exponential&#x27;, n_estimators=150)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(loss='exponential', n_estimators=150)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier(learning_rate = 0.1, loss = 'exponential', n_estimators = 150)\n",
    "gbc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9983552631578947\n",
      "0.9144736842105263\n",
      "[[91  7]\n",
      " [ 6 48]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.93      0.93        98\n",
      "           1       0.87      0.89      0.88        54\n",
      "\n",
      "    accuracy                           0.91       152\n",
      "   macro avg       0.91      0.91      0.91       152\n",
      "weighted avg       0.91      0.91      0.91       152\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gbc = grid_search_gbc.best_estimator_\n",
    "y_pred = gbc.predict(X_test)\n",
    "print(accuracy_score(y_train, gbc.predict(X_train)))\n",
    "gbc_acc = accuracy_score(y_test, gbc.predict(X_test))\n",
    "print(accuracy_score(y_test, gbc.predict(X_test)))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.01, max_bin=256,\n",
       "              max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "              max_depth=10, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints=&#x27;()&#x27;, n_estimators=180, n_jobs=0,\n",
       "              num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.01, max_bin=256,\n",
       "              max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "              max_depth=10, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints=&#x27;()&#x27;, n_estimators=180, n_jobs=0,\n",
       "              num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy='depthwise', importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.01, max_bin=256,\n",
       "              max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "              max_depth=10, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=180, n_jobs=0,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0, ...)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier \n",
    "xgb = XGBClassifier(objective = 'binary:logistic', learning_rate = 0.01, max_depth = 10, n_estimators = 180)\n",
    "\n",
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9868421052631579\n",
      "0.881578947368421\n",
      "[[88 10]\n",
      " [ 8 46]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.90      0.91        98\n",
      "           1       0.82      0.85      0.84        54\n",
      "\n",
      "    accuracy                           0.88       152\n",
      "   macro avg       0.87      0.87      0.87       152\n",
      "weighted avg       0.88      0.88      0.88       152\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = xgb.predict(X_test)\n",
    "print(accuracy_score(y_train, xgb.predict(X_train)))\n",
    "xgb_acc = accuracy_score(y_test, xgb.predict(X_test))\n",
    "print(accuracy_score(y_test, xgb.predict(X_test)))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>91.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVM</td>\n",
       "      <td>90.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>89.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>89.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>89.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>88.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XgBoost</td>\n",
       "      <td>88.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Model  Score\n",
       "5  Gradient Boosting Classifier  91.45\n",
       "2                           SVM  90.79\n",
       "0           Logistic Regression  89.47\n",
       "3      Decision Tree Classifier  89.47\n",
       "4      Random Forest Classifier  89.47\n",
       "1                           KNN  88.16\n",
       "6                       XgBoost  88.16"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Model Comparison\n",
    "models = pd.DataFrame({\n",
    "    'Model': ['Logistic Regression', 'KNN', 'SVM', 'Decision Tree Classifier', 'Random Forest Classifier', 'Gradient Boosting Classifier', 'XgBoost'],\n",
    "    'Score': [100*round(log_reg_acc,4), 100*round(knn_acc,4), 100*round(svc_acc,4), 100*round(dt_acc,4), 100*round(rand_acc,4), \n",
    "              100*round(gbc_acc,4), 100*round(xgb_acc,4)]\n",
    "})\n",
    "models.sort_values(by = 'Score', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "model = gbc_acc\n",
    "pickle.dump(model, open(\"diabetes.pkl\",'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "plt.figure(figsize=(8,5))\n",
    "models = [\n",
    "{\n",
    "    'label': 'LR',\n",
    "    'model': log_reg,\n",
    "},\n",
    "{\n",
    "    'label': 'DT',\n",
    "    'model': DT,\n",
    "},\n",
    "{\n",
    "    'label': 'SVM',\n",
    "    'model': svc,\n",
    "},\n",
    "{\n",
    "    'label': 'KNN',\n",
    "    'model': knn,\n",
    "},\n",
    "{\n",
    "    'label': 'XGBoost',\n",
    "    'model': xgb,\n",
    "},\n",
    "{\n",
    "    'label': 'RF',\n",
    "    'model': rand_clf,\n",
    "},\n",
    "{\n",
    "    'label': 'GBDT',\n",
    "    'model': gbc,\n",
    "}\n",
    "]\n",
    "for m in models:\n",
    "    model = m['model'] \n",
    "    model.fit(X_train, y_train) \n",
    "    y_pred=model.predict(X_test) \n",
    "    fpr1, tpr1, thresholds = metrics.roc_curve(y_test, model.predict_proba(X_test)[:,1])\n",
    "    auc = metrics.roc_auc_score(y_test,model.predict(X_test))\n",
    "    plt.plot(fpr1, tpr1, label='%s - ROC (area = %0.2f)' % (m['label'], auc))\n",
    "\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([-0.01, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('1 - Specificity (False Positive Rate)', fontsize=12)\n",
    "plt.ylabel('Sensitivity (True Positive Rate)', fontsize=12)\n",
    "plt.title('ROC - Diabetes Prediction', fontsize=12)\n",
    "plt.legend(loc=\"lower right\", fontsize=12)\n",
    "plt.savefig(\"roc_diabetes.jpeg\", format='jpeg', dpi=400, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[89.47, 89.47, 90.79, 88.16000000000001, 88.16000000000001, 89.47, 91.45]\n",
      "[89.34, 82.89999999999999, 90.36, 87.49, 87.49, 89.34, 90.86999999999999]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "models = [\n",
    "{\n",
    "    'label': 'LR',\n",
    "    'model': log_reg,\n",
    "},\n",
    "{\n",
    "    'label': 'DT',\n",
    "    'model': DT,\n",
    "},\n",
    "{\n",
    "    'label': 'SVM',\n",
    "    'model': svc,\n",
    "},\n",
    "{\n",
    "    'label': 'KNN',\n",
    "    'model': knn,\n",
    "},\n",
    "{\n",
    "    'label': 'XGBoost',\n",
    "    'model': xgb,\n",
    "},\n",
    "{\n",
    "    'label': 'RF',\n",
    "    'model': rand_clf,\n",
    "},\n",
    "{\n",
    "    'label': 'GBDT',\n",
    "    'model': gbc,\n",
    "}\n",
    "]\n",
    "\n",
    "means_roc = []\n",
    "means_accuracy = [100*round(log_reg_acc,4), 100*round(dt_acc,4), 100*round(svc_acc,4), 100*round(knn_acc,4), 100*round(xgb_acc,4), \n",
    "                  100*round(rand_acc,4), 100*round(gbc_acc,4)]\n",
    "\n",
    "for m in models:\n",
    "    model = m['model'] \n",
    "    model.fit(X_train, y_train) \n",
    "    y_pred=model.predict(X_test) \n",
    "    fpr1, tpr1, thresholds = metrics.roc_curve(y_test, model.predict_proba(X_test)[:,1])\n",
    "    auc = metrics.roc_auc_score(y_test,model.predict(X_test))\n",
    "    auc = 100*round(auc,4)\n",
    "    means_roc.append(auc)\n",
    "\n",
    "print(means_accuracy)\n",
    "print(means_roc)\n",
    "\n",
    "# data to plot\n",
    "n_groups = 7\n",
    "means_accuracy = tuple(means_accuracy)\n",
    "means_roc = tuple(means_roc)\n",
    "\n",
    "# create plot\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    "\n",
    "rects1 = plt.bar(index, means_accuracy, bar_width,\n",
    "alpha=opacity,\n",
    "color='mediumpurple',\n",
    "label='Accuracy (%)')\n",
    "\n",
    "rects2 = plt.bar(index + bar_width, means_roc, bar_width,\n",
    "alpha=opacity,\n",
    "color='rebeccapurple',\n",
    "label='ROC (%)')\n",
    "\n",
    "plt.xlim([-1, 8])\n",
    "plt.ylim([60, 95])\n",
    "\n",
    "plt.title('Performance Evaluation - Diabetes Prediction', fontsize=12)\n",
    "plt.xticks(index, ('   LR', '   DT', '   SVM', '   KNN', 'XGBoost' , '   RF', '   GBDT'), rotation=40, ha='center', fontsize=12)\n",
    "plt.legend(loc=\"upper right\", fontsize=10)\n",
    "plt.savefig(\"PE_diabetes.jpeg\", format='jpeg', dpi=400, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.90625\n",
      "0.8947368421052632\n",
      "[[92  6]\n",
      " [10 44]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.94      0.92        98\n",
      "           1       0.88      0.81      0.85        54\n",
      "\n",
      "    accuracy                           0.89       152\n",
      "   macro avg       0.89      0.88      0.88       152\n",
      "weighted avg       0.89      0.89      0.89       152\n",
      "\n"
     ]
    }
   ],
   "source": [
    "DT = grid_search_dt.best_estimator_\n",
    "y_pred = DT.predict(X_test)\n",
    "print(accuracy_score(y_train, DT.predict(X_train)))\n",
    "dt_acc = accuracy_score(y_test, DT.predict(X_test))\n",
    "print(accuracy_score(y_test, DT.predict(X_test)))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.8486842105263158\n",
      "[[84 14]\n",
      " [ 9 45]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.86      0.88        98\n",
      "           1       0.76      0.83      0.80        54\n",
      "\n",
      "    accuracy                           0.85       152\n",
      "   macro avg       0.83      0.85      0.84       152\n",
      "weighted avg       0.85      0.85      0.85       152\n",
      "\n"
     ]
    }
   ],
   "source": [
    "DT = DecisionTreeClassifier()\n",
    "DT.fit(X_train, y_train)\n",
    "y_pred = DT.predict(X_test)\n",
    "print(accuracy_score(y_train, DT.predict(X_train)))\n",
    "\n",
    "print(accuracy_score(y_test, DT.predict(X_test)))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 50 folds for each of 1200 candidates, totalling 60000 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=50, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 7, 10],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 3, 5, 7],\n",
       "                         &#x27;min_samples_split&#x27;: [1, 2, 3, 5, 7],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;radom&#x27;]},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=50, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 7, 10],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 3, 5, 7],\n",
       "                         &#x27;min_samples_split&#x27;: [1, 2, 3, 5, 7],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;radom&#x27;]},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=50, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [3, 5, 7, 10],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'min_samples_leaf': [1, 2, 3, 5, 7],\n",
       "                         'min_samples_split': [1, 2, 3, 5, 7],\n",
       "                         'splitter': ['best', 'radom']},\n",
       "             verbose=1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hyperparameter tuning of dt\n",
    "grid_param = {\n",
    "    'criterion':['gini','entropy'],\n",
    "    'max_depth' :  [3,5,7,10],\n",
    "    'splitter' : ['best','radom'],\n",
    "    'min_samples_leaf':[1,2,3,5,7], \n",
    "    'min_samples_split':[1,2,3,5,7], \n",
    "    'max_features':['auto','sqrt','log2']\n",
    "}\n",
    "grid_search_dt = GridSearchCV(DT, grid_param, cv=50, n_jobs=-1, verbose = 1)\n",
    "grid_search_dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini',\n",
       " 'max_depth': 10,\n",
       " 'max_features': 'sqrt',\n",
       " 'min_samples_leaf': 5,\n",
       " 'min_samples_split': 7,\n",
       " 'splitter': 'best'}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_search_dt.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8470394736842105"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_score(y_train, log_reg.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg_acc = accuracy_score(y_test, log_reg.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[88, 10],\n",
       "       [ 6, 48]], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.90      0.92        98\n",
      "           1       0.83      0.89      0.86        54\n",
      "\n",
      "    accuracy                           0.89       152\n",
      "   macro avg       0.88      0.89      0.89       152\n",
      "weighted avg       0.90      0.89      0.90       152\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
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}