DD_Model_Selection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7ea79d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import pylab\n",
    "import scipy.stats as stat\n",
    "from scipy.stats import ttest_ind, chi2_contingency\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "from category_encoders import TargetEncoder\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler, RobustScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50e42bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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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>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>0.865276</td>\n",
       "      <td>-0.005124</td>\n",
       "      <td>0.928458</td>\n",
       "      <td>-0.815354</td>\n",
       "      <td>0.217978</td>\n",
       "      <td>0.798645</td>\n",
       "      <td>1.556957</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.205989</td>\n",
       "      <td>-0.542772</td>\n",
       "      <td>0.546002</td>\n",
       "      <td>-0.815354</td>\n",
       "      <td>-0.874679</td>\n",
       "      <td>-0.328392</td>\n",
       "      <td>-0.163949</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.015979</td>\n",
       "      <td>-0.721988</td>\n",
       "      <td>-1.302538</td>\n",
       "      <td>-0.815354</td>\n",
       "      <td>-1.389789</td>\n",
       "      <td>0.982401</td>\n",
       "      <td>-0.073375</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.074480</td>\n",
       "      <td>-0.542772</td>\n",
       "      <td>0.163545</td>\n",
       "      <td>0.399244</td>\n",
       "      <td>-0.640538</td>\n",
       "      <td>-1.079749</td>\n",
       "      <td>-1.069690</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.503626</td>\n",
       "      <td>-2.872582</td>\n",
       "      <td>0.928458</td>\n",
       "      <td>1.355416</td>\n",
       "      <td>1.700869</td>\n",
       "      <td>0.165203</td>\n",
       "      <td>0.017199</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>-0.679954</td>\n",
       "      <td>0.353308</td>\n",
       "      <td>1.757114</td>\n",
       "      <td>1.510471</td>\n",
       "      <td>0.108712</td>\n",
       "      <td>-1.063415</td>\n",
       "      <td>2.734419</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>0.010468</td>\n",
       "      <td>-0.184340</td>\n",
       "      <td>0.418516</td>\n",
       "      <td>-0.815354</td>\n",
       "      <td>0.717478</td>\n",
       "      <td>-0.373310</td>\n",
       "      <td>-0.526245</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>-0.022409</td>\n",
       "      <td>-0.005124</td>\n",
       "      <td>0.163545</td>\n",
       "      <td>0.631826</td>\n",
       "      <td>-0.937117</td>\n",
       "      <td>-0.761239</td>\n",
       "      <td>-0.254523</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>0.141977</td>\n",
       "      <td>-1.080421</td>\n",
       "      <td>-1.302538</td>\n",
       "      <td>-0.815354</td>\n",
       "      <td>-0.328351</td>\n",
       "      <td>-0.336558</td>\n",
       "      <td>1.285235</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>-0.942972</td>\n",
       "      <td>-0.184340</td>\n",
       "      <td>0.673487</td>\n",
       "      <td>-0.815354</td>\n",
       "      <td>-0.281523</td>\n",
       "      <td>-0.475396</td>\n",
       "      <td>-0.888542</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "0    0.865276      -0.005124       0.928458 -0.815354  0.217978   \n",
       "1   -1.205989      -0.542772       0.546002 -0.815354 -0.874679   \n",
       "2    2.015979      -0.721988      -1.302538 -0.815354 -1.389789   \n",
       "3   -1.074480      -0.542772       0.163545  0.399244 -0.640538   \n",
       "4    0.503626      -2.872582       0.928458  1.355416  1.700869   \n",
       "..        ...            ...            ...       ...       ...   \n",
       "763 -0.679954       0.353308       1.757114  1.510471  0.108712   \n",
       "764  0.010468      -0.184340       0.418516 -0.815354  0.717478   \n",
       "765 -0.022409      -0.005124       0.163545  0.631826 -0.937117   \n",
       "766  0.141977      -1.080421      -1.302538 -0.815354 -0.328351   \n",
       "767 -0.942972      -0.184340       0.673487 -0.815354 -0.281523   \n",
       "\n",
       "     DiabetesPedigreeFunction       Age  Outcome  \n",
       "0                    0.798645  1.556957      1.0  \n",
       "1                   -0.328392 -0.163949      0.0  \n",
       "2                    0.982401 -0.073375      1.0  \n",
       "3                   -1.079749 -1.069690      0.0  \n",
       "4                    0.165203  0.017199      1.0  \n",
       "..                        ...       ...      ...  \n",
       "763                 -1.063415  2.734419      0.0  \n",
       "764                 -0.373310 -0.526245      0.0  \n",
       "765                 -0.761239 -0.254523      0.0  \n",
       "766                 -0.336558  1.285235      1.0  \n",
       "767                 -0.475396 -0.888542      0.0  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir(\"C:\\\\Users\\\\Nilesh\\\\Documents\\\\GitHub\\\\01 Diabetes Prediction\")\n",
    "df = pd.read_csv(\"Diabetes-dataset_FT.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d793073",
   "metadata": {},
   "source": [
    "## Seperating dependent and independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d535fc3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.86527574, -0.00512393,  0.92845833, ...,  0.21797752,\n",
       "         0.79864498,  1.55695702],\n",
       "       [-1.20598931, -0.54277224,  0.5460018 , ..., -0.87467912,\n",
       "        -0.32839152, -0.16394935],\n",
       "       [ 2.01597855, -0.72198835, -1.30253807, ..., -1.38978867,\n",
       "         0.98240093, -0.07337533],\n",
       "       ...,\n",
       "       [-0.02240928, -0.00512393,  0.16354528, ..., -0.93711664,\n",
       "        -0.76123887, -0.25452337],\n",
       "       [ 0.14197684, -1.08042056, -1.30253807, ..., -0.3283508 ,\n",
       "        -0.33655845,  1.28523497],\n",
       "       [-0.94297153, -0.18434003,  0.67348731, ..., -0.28152266,\n",
       "        -0.47539628, -0.88854151]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:,:-1].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "699b199b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       1., 1., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 1., 1.,\n",
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       "       0., 1., 0., 1., 0., 0., 0., 0., 0., 1., 0., 1., 0., 1., 0., 1., 1.,\n",
       "       0., 0., 0., 0., 1., 1., 0., 0., 0., 1., 0., 1., 1., 0., 0., 1., 0.,\n",
       "       0., 1., 1., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
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       "       0., 1., 0.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df.iloc[:,-1].values\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a674df",
   "metadata": {},
   "source": [
    "## Test-train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39d06b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.66, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6592fe37",
   "metadata": {},
   "source": [
    "## Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc1c4475",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef6152f5",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e7222b79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------\n",
      "Report\n",
      "----------------------------------------------------------------------\n",
      "Confusion Matrix:\n",
      "[[152  13]\n",
      " [ 38  59]]\n",
      "Accuracy by confusion matrix: 0.8053435114503816\n",
      "\n",
      "\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "without diabetes       0.80      0.92      0.86       165\n",
      "   with diabetes       0.82      0.61      0.70        97\n",
      "\n",
      "        accuracy                           0.81       262\n",
      "       macro avg       0.81      0.76      0.78       262\n",
      "    weighted avg       0.81      0.81      0.80       262\n",
      "\n",
      "----------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression(random_state=16)\n",
    "lr.fit(X_train, Y_train)\n",
    "Y_pred = lr.predict(X_test)\n",
    "confusion_matrix = metrics.confusion_matrix(Y_test, Y_pred)\n",
    "print(\"-\"*70)\n",
    "print(\"Report\")\n",
    "print(\"-\"*70)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(str(confusion_matrix))\n",
    "target_names = ['without diabetes', 'with diabetes']\n",
    "acc=(confusion_matrix[0][0] + confusion_matrix[1][1])/(confusion_matrix[0][0]+confusion_matrix[0][1]+confusion_matrix[1][0]+confusion_matrix[1][1])\n",
    "print(\"Accuracy by confusion matrix: \"+str(acc))\n",
    "print(\"\\n\")\n",
    "print(classification_report(Y_test, Y_pred, target_names=target_names))\n",
    "print(\"-\"*70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a1b989",
   "metadata": {},
   "source": [
    "### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c2a5266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------\n",
      "Report\n",
      "----------------------------------------------------------------------\n",
      "Confusion Matrix:\n",
      "[[147  18]\n",
      " [ 42  55]]\n",
      "Accuracy by confusion matrix: 0.7709923664122137\n",
      "\n",
      "\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "without diabetes       0.78      0.89      0.83       165\n",
      "   with diabetes       0.75      0.57      0.65        97\n",
      "\n",
      "        accuracy                           0.77       262\n",
      "       macro avg       0.77      0.73      0.74       262\n",
      "    weighted avg       0.77      0.77      0.76       262\n",
      "\n",
      "----------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, Y_train)\n",
    "Y_pred = gnb.predict(X_test)\n",
    "confusion_matrix = metrics.confusion_matrix(Y_test, Y_pred)\n",
    "print(\"-\"*70)\n",
    "print(\"Report\")\n",
    "print(\"-\"*70)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(str(confusion_matrix))\n",
    "target_names = ['without diabetes', 'with diabetes']\n",
    "acc=(confusion_matrix[0][0] + confusion_matrix[1][1])/(confusion_matrix[0][0]+confusion_matrix[0][1]+confusion_matrix[1][0]+confusion_matrix[1][1])\n",
    "print(\"Accuracy by confusion matrix: \"+str(acc))\n",
    "print(\"\\n\")\n",
    "print(classification_report(Y_test, Y_pred, target_names=target_names))\n",
    "print(\"-\"*70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7bd50a2",
   "metadata": {},
   "source": [
    "### K-nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "838def6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------\n",
      "Report\n",
      "----------------------------------------------------------------------\n",
      "Confusion Matrix:\n",
      "[[139  26]\n",
      " [ 43  54]]\n",
      "Accuracy by confusion matrix: 0.7366412213740458\n",
      "\n",
      "\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "without diabetes       0.76      0.84      0.80       165\n",
      "   with diabetes       0.68      0.56      0.61        97\n",
      "\n",
      "        accuracy                           0.74       262\n",
      "       macro avg       0.72      0.70      0.71       262\n",
      "    weighted avg       0.73      0.74      0.73       262\n",
      "\n",
      "----------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train,Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "confusion_matrix = metrics.confusion_matrix(Y_test, Y_pred)\n",
    "print(\"-\"*70)\n",
    "print(\"Report\")\n",
    "print(\"-\"*70)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(str(confusion_matrix))\n",
    "target_names = ['without diabetes', 'with diabetes']\n",
    "acc=(confusion_matrix[0][0] + confusion_matrix[1][1])/(confusion_matrix[0][0]+confusion_matrix[0][1]+confusion_matrix[1][0]+confusion_matrix[1][1])\n",
    "print(\"Accuracy by confusion matrix: \"+str(acc))\n",
    "print(\"\\n\")\n",
    "print(classification_report(Y_test, Y_pred, target_names=target_names))\n",
    "print(\"-\"*70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43efdf12",
   "metadata": {},
   "source": [
    "### Decision tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "340a1d70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------\n",
      "Report\n",
      "----------------------------------------------------------------------\n",
      "Confusion Matrix:\n",
      "[[129  36]\n",
      " [ 32  65]]\n",
      "Accuracy by confusion matrix: 0.7404580152671756\n",
      "\n",
      "\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "without diabetes       0.80      0.78      0.79       165\n",
      "   with diabetes       0.64      0.67      0.66        97\n",
      "\n",
      "        accuracy                           0.74       262\n",
      "       macro avg       0.72      0.73      0.72       262\n",
      "    weighted avg       0.74      0.74      0.74       262\n",
      "\n",
      "----------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train,Y_train)\n",
    "Y_pred = dt.predict(X_test)\n",
    "confusion_matrix = metrics.confusion_matrix(Y_test, Y_pred)\n",
    "print(\"-\"*70)\n",
    "print(\"Report\")\n",
    "print(\"-\"*70)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(str(confusion_matrix))\n",
    "target_names = ['without diabetes', 'with diabetes']\n",
    "acc=(confusion_matrix[0][0] + confusion_matrix[1][1])/(confusion_matrix[0][0]+confusion_matrix[0][1]+confusion_matrix[1][0]+confusion_matrix[1][1])\n",
    "print(\"Accuracy by confusion matrix: \"+str(acc))\n",
    "print(\"\\n\")\n",
    "print(classification_report(Y_test, Y_pred, target_names=target_names))\n",
    "print(\"-\"*70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76cff415",
   "metadata": {},
   "source": [
    "### Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "74b8dd9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------\n",
      "Report\n",
      "----------------------------------------------------------------------\n",
      "Confusion Matrix:\n",
      "[[158   7]\n",
      " [ 46  51]]\n",
      "Accuracy by confusion matrix: 0.7977099236641222\n",
      "\n",
      "\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "without diabetes       0.77      0.96      0.86       165\n",
      "   with diabetes       0.88      0.53      0.66        97\n",
      "\n",
      "        accuracy                           0.80       262\n",
      "       macro avg       0.83      0.74      0.76       262\n",
      "    weighted avg       0.81      0.80      0.78       262\n",
      "\n",
      "----------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "svm = svm.SVC(kernel='poly') \n",
    "svm.fit(X_train, Y_train)\n",
    "Y_pred = svm.predict(X_test)\n",
    "confusion_matrix = metrics.confusion_matrix(Y_test, Y_pred)\n",
    "print(\"-\"*70)\n",
    "print(\"Report\")\n",
    "print(\"-\"*70)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(str(confusion_matrix))\n",
    "target_names = ['without diabetes', 'with diabetes']\n",
    "acc=(confusion_matrix[0][0] + confusion_matrix[1][1])/(confusion_matrix[0][0]+confusion_matrix[0][1]+confusion_matrix[1][0]+confusion_matrix[1][1])\n",
    "print(\"Accuracy by confusion matrix: \"+str(acc))\n",
    "print(\"\\n\")\n",
    "print(classification_report(Y_test, Y_pred, target_names=target_names))\n",
    "print(\"-\"*70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0103678d",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d62123f",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8de1fe15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.770825 using {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.768210 (0.041727) with: {'C': 100, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.768210 (0.041727) with: {'C': 100, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.768210 (0.041727) with: {'C': 100, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.768210 (0.041727) with: {'C': 10, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.768210 (0.041727) with: {'C': 10, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.768210 (0.041727) with: {'C': 10, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.768643 (0.041093) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.768643 (0.041093) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.767777 (0.041135) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.770398 (0.041642) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.770398 (0.041642) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.770825 (0.041311) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.758675 (0.043801) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.758675 (0.043801) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.766103 (0.044868) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'liblinear'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "model = LogisticRegression()\n",
    "solvers = ['newton-cg', 'lbfgs', 'liblinear']\n",
    "penalty = ['l2']\n",
    "c_values = [100, 10, 1.0, 0.1, 0.01]\n",
    "# define grid search\n",
    "grid = dict(solver=solvers,penalty=penalty,C=c_values)\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=cv, scoring='accuracy',error_score=0)\n",
    "grid_result = grid_search.fit(X, Y)\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b1d5be4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------\n",
      "Report\n",
      "----------------------------------------------------------------------\n",
      "Confusion Matrix:\n",
      "[[445  55]\n",
      " [117 151]]\n",
      "Accuracy by confusion matrix: 0.7760416666666666\n",
      "\n",
      "\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "without diabetes       0.79      0.89      0.84       500\n",
      "   with diabetes       0.73      0.56      0.64       268\n",
      "\n",
      "        accuracy                           0.78       768\n",
      "       macro avg       0.76      0.73      0.74       768\n",
      "    weighted avg       0.77      0.78      0.77       768\n",
      "\n",
      "----------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(random_state=16,C=0.1,penalty='l2',\n",
    "    dual=False,\n",
    "    tol=0.0001,\n",
    "    fit_intercept=True,\n",
    "    intercept_scaling=1,\n",
    "    class_weight=None,\n",
    "    solver='liblinear',\n",
    "    max_iter=100,\n",
    "    multi_class='auto',\n",
    "    verbose=0,\n",
    "    warm_start=False,\n",
    "    n_jobs=None,\n",
    "    l1_ratio=None,)\n",
    "\n",
    "lr.fit(X_train, Y_train)\n",
    "Y_pred = lr.predict(X)\n",
    "confusion_matrix = metrics.confusion_matrix(Y, Y_pred)\n",
    "print(\"-\"*70)\n",
    "print(\"Report\")\n",
    "print(\"-\"*70)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(str(confusion_matrix))\n",
    "target_names = ['without diabetes', 'with diabetes']\n",
    "acc=(confusion_matrix[0][0] + confusion_matrix[1][1])/(confusion_matrix[0][0]+confusion_matrix[0][1]+confusion_matrix[1][0]+confusion_matrix[1][1])\n",
    "print(\"Accuracy by confusion matrix: \"+str(acc))\n",
    "print(\"\\n\")\n",
    "print(classification_report(Y, Y_pred, target_names=target_names))\n",
    "print(\"-\"*70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b906aaf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(lr, open('lr.pkl','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b60e30",
   "metadata": {},
   "source": [
    "## Evaluating Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3d18a774",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"C:\\\\Users\\\\Nilesh\\\\Documents\\\\GitHub\\\\01 Diabetes Prediction\")\n",
    "df_copy= pd.read_csv(\"diabetes-preprocessed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c97f9971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>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>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351000</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>101.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>122.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>121.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>126.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>93.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0      148.0           72.0           35.0      0.0  33.6   \n",
       "1       85.0           66.0           29.0      0.0  26.6   \n",
       "2      183.0           64.0            0.0      0.0  23.3   \n",
       "3       89.0           66.0           23.0     94.0  28.1   \n",
       "4      137.0           40.0           35.0    168.0  43.1   \n",
       "..       ...            ...            ...      ...   ...   \n",
       "763    101.0           76.0           48.0    180.0  32.9   \n",
       "764    122.0           70.0           27.0      0.0  36.8   \n",
       "765    121.0           72.0           23.0    112.0  26.2   \n",
       "766    126.0           60.0            0.0      0.0  30.1   \n",
       "767     93.0           70.0           31.0      0.0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction   Age  Outcome  \n",
       "0                    0.627000  50.0      1.0  \n",
       "1                    0.351000  31.0      0.0  \n",
       "2                    0.672000  32.0      1.0  \n",
       "3                    0.167000  21.0      0.0  \n",
       "4                    0.471876  33.0      1.0  \n",
       "..                        ...   ...      ...  \n",
       "763                  0.171000  63.0      0.0  \n",
       "764                  0.340000  27.0      0.0  \n",
       "765                  0.245000  30.0      0.0  \n",
       "766                  0.349000  47.0      1.0  \n",
       "767                  0.315000  23.0      0.0  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "da5fbc51",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = pd.DataFrame(Y_pred, columns = ['Y_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e90cc951",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>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>Y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351000</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>101.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>122.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>121.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>126.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>93.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0      148.0           72.0           35.0      0.0  33.6   \n",
       "1       85.0           66.0           29.0      0.0  26.6   \n",
       "2      183.0           64.0            0.0      0.0  23.3   \n",
       "3       89.0           66.0           23.0     94.0  28.1   \n",
       "4      137.0           40.0           35.0    168.0  43.1   \n",
       "..       ...            ...            ...      ...   ...   \n",
       "763    101.0           76.0           48.0    180.0  32.9   \n",
       "764    122.0           70.0           27.0      0.0  36.8   \n",
       "765    121.0           72.0           23.0    112.0  26.2   \n",
       "766    126.0           60.0            0.0      0.0  30.1   \n",
       "767     93.0           70.0           31.0      0.0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction   Age  Outcome  Y_pred  \n",
       "0                    0.627000  50.0      1.0     1.0  \n",
       "1                    0.351000  31.0      0.0     0.0  \n",
       "2                    0.672000  32.0      1.0     1.0  \n",
       "3                    0.167000  21.0      0.0     0.0  \n",
       "4                    0.471876  33.0      1.0     1.0  \n",
       "..                        ...   ...      ...     ...  \n",
       "763                  0.171000  63.0      0.0     0.0  \n",
       "764                  0.340000  27.0      0.0     0.0  \n",
       "765                  0.245000  30.0      0.0     0.0  \n",
       "766                  0.349000  47.0      1.0     0.0  \n",
       "767                  0.315000  23.0      0.0     0.0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = pd.concat([df_copy, Y_pred], axis=1)\n",
    "df_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3b4be9fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results.to_csv('Diabetes-dataset_RS.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4925d1e",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "## Visualizing Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9e44c94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataplot = sns.heatmap(confusion_matrix, cmap='PuRd', annot=True, center=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "89b0c99a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Predicted'}, xlabel='Glucose', ylabel='DiabetesPedigreeFunction'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [10.50, 4.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "figure, axis = plt.subplots(1, 2)\n",
    "\n",
    "colors = {0:'MEDIUMVIOLETRED', 1:'INDIGO'}\n",
    " \n",
    "df_results.plot.scatter(\"Glucose\",\"DiabetesPedigreeFunction\",title=\"Acctual\",ax=axis[0],c=df_results['Outcome'].map(colors))\n",
    "df_results.plot.scatter(\"Glucose\",\"DiabetesPedigreeFunction\",title=\"Predicted\",ax=axis[1],c=df_results['Y_pred'].map(colors))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}