Diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# PIMA Indians Diabetes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. \n",
    "\n",
    "The objective is to predict based on diagnostic measurements whether a patient has diabetes.\n",
    "\n",
    "Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.\n",
    "\n",
    "- **Pregnancies:** Number of times pregnant\n",
    "- **Glucose:** Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
    "- **BloodPressure:** Diastolic blood pressure (mm Hg)\n",
    "- **SkinThickness:** Triceps skin fold thickness (mm)\n",
    "- **Insulin:** 2-Hour serum insulin (mu U/ml)\n",
    "- **BMI:** Body mass index (weight in kg/(height in m)^2)\n",
    "- **DiabetesPedigreeFunction:** Diabetes pedigree function\n",
    "- **Age:** Age (years)\n",
    "- **Outcome:** Class variable (0 or 1) 1: Diabetic, 0: Healty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Libraries import\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from numpy import sqrt\n",
    "from numpy import argmax\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score, classification_report, mean_squared_error, roc_curve, RocCurveDisplay, recall_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, KFold\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dataset read\n",
    "pima = pd.read_csv('diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The dataset has 768 rows and 9 columns\n",
    "pima.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
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       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "#This output shows that all dataset features are numeric.\n",
    "pima.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>768.0</td>\n",
       "      <td>3.845052</td>\n",
       "      <td>3.369578</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>768.0</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>0.000</td>\n",
       "      <td>99.00000</td>\n",
       "      <td>117.0000</td>\n",
       "      <td>140.25000</td>\n",
       "      <td>199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>768.0</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>0.000</td>\n",
       "      <td>62.00000</td>\n",
       "      <td>72.0000</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>122.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>768.0</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>768.0</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>127.25000</td>\n",
       "      <td>846.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>768.0</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.30000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>36.60000</td>\n",
       "      <td>67.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.24375</td>\n",
       "      <td>0.3725</td>\n",
       "      <td>0.62625</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>768.0</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>21.000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.348958</td>\n",
       "      <td>0.476951</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count        mean         std     min       25%  \\\n",
       "Pregnancies               768.0    3.845052    3.369578   0.000   1.00000   \n",
       "Glucose                   768.0  120.894531   31.972618   0.000  99.00000   \n",
       "BloodPressure             768.0   69.105469   19.355807   0.000  62.00000   \n",
       "SkinThickness             768.0   20.536458   15.952218   0.000   0.00000   \n",
       "Insulin                   768.0   79.799479  115.244002   0.000   0.00000   \n",
       "BMI                       768.0   31.992578    7.884160   0.000  27.30000   \n",
       "DiabetesPedigreeFunction  768.0    0.471876    0.331329   0.078   0.24375   \n",
       "Age                       768.0   33.240885   11.760232  21.000  24.00000   \n",
       "Outcome                   768.0    0.348958    0.476951   0.000   0.00000   \n",
       "\n",
       "                               50%        75%     max  \n",
       "Pregnancies                 3.0000    6.00000   17.00  \n",
       "Glucose                   117.0000  140.25000  199.00  \n",
       "BloodPressure              72.0000   80.00000  122.00  \n",
       "SkinThickness              23.0000   32.00000   99.00  \n",
       "Insulin                    30.5000  127.25000  846.00  \n",
       "BMI                        32.0000   36.60000   67.10  \n",
       "DiabetesPedigreeFunction    0.3725    0.62625    2.42  \n",
       "Age                        29.0000   41.00000   81.00  \n",
       "Outcome                     0.0000    1.00000    1.00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This function show the main statistics for the features. \n",
    "pima.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This function shows that any of the features has null values\n",
    "pima.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlations\n",
    "Each square shows the correlation between the variables on each axis. Correlation ranges from -1 to +1. \n",
    "- Values closer to 0: There is no linear trend between the two variables. \n",
    "- Values closer to 1: The correlation is positive; that is as one increases so does the other and the closer to 1 the stronger this relationship is. \n",
    "- Values closer to -1: One variable will decrease as the other increases\n",
    "\n",
    "The diagonals are all 1/dark because those squares are correlating each variable to itself (so it's a perfect correlation). \n",
    "For the rest the larger the number and darker the color the higher the correlation between the two variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "correlation = pima.corr()\n",
    "plt.figure(figsize = (8,7))\n",
    "sns.heatmap(correlation, cmap = cmap, annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation matrix:\n",
    "- There is a strong positve correlation between Pregnancies and Age, Glucose and Outcome.\n",
    "- There is a strong negative correlation between Skinthickness and Age, Pregnancies and SkinThickness, Pregnance and Insulin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome                     1.000000\n",
       "Glucose                     0.466581\n",
       "BMI                         0.292695\n",
       "Age                         0.238356\n",
       "Pregnancies                 0.221898\n",
       "DiabetesPedigreeFunction    0.173844\n",
       "Insulin                     0.130548\n",
       "SkinThickness               0.074752\n",
       "BloodPressure               0.065068\n",
       "Name: Outcome, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation of features with the Outcome\n",
    "corr_report = pima.corr()['Outcome']\n",
    "corr_report.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Glucose is the most correlated feature and BloodPressure is the least correlated feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>168</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.537</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>846</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.398</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>166</td>\n",
       "      <td>72</td>\n",
       "      <td>19</td>\n",
       "      <td>175</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0.587</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>6</td>\n",
       "      <td>162</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0.178</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>0</td>\n",
       "      <td>181</td>\n",
       "      <td>88</td>\n",
       "      <td>44</td>\n",
       "      <td>510</td>\n",
       "      <td>43.3</td>\n",
       "      <td>0.222</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>8</td>\n",
       "      <td>154</td>\n",
       "      <td>78</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.443</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>6</td>\n",
       "      <td>190</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.278</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>9</td>\n",
       "      <td>170</td>\n",
       "      <td>74</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.403</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "2              8      183             64              0        0  23.3   \n",
       "8              2      197             70             45      543  30.5   \n",
       "11            10      168             74              0        0  38.0   \n",
       "13             1      189             60             23      846  30.1   \n",
       "14             5      166             72             19      175  25.8   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "749            6      162             62              0        0  24.3   \n",
       "753            0      181             88             44      510  43.3   \n",
       "754            8      154             78             32        0  32.4   \n",
       "759            6      190             92              0        0  35.5   \n",
       "761            9      170             74             31        0  44.0   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "2                       0.672   32        1  \n",
       "8                       0.158   53        1  \n",
       "11                      0.537   34        1  \n",
       "13                      0.398   59        1  \n",
       "14                      0.587   51        1  \n",
       "..                        ...  ...      ...  \n",
       "749                     0.178   50        1  \n",
       "753                     0.222   26        1  \n",
       "754                     0.443   45        1  \n",
       "759                     0.278   66        1  \n",
       "761                     0.403   43        1  \n",
       "\n",
       "[140 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers = pima[pima['Glucose'] > 150]\n",
    "outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    105\n",
       "0     35\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers['Outcome'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "75% of patients with Glucose higher than 150, has diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram to analyze the distribution of data\n",
    "fig, ax = plt.subplots(ncols=3, nrows=3, figsize=(15,12))\n",
    "index = 0\n",
    "ax = ax.flatten()\n",
    "\n",
    "for col, value in pima.items():\n",
    "    col_dist = sns.histplot(value, ax=ax[index], kde=True, stat=\"density\", linewidth=0)\n",
    "    col_dist.set_xlabel(col,fontsize=10)\n",
    "    col_dist.set_ylabel('density',fontsize=10)\n",
    "    index += 1\n",
    "plt.tight_layout(pad=0.8, w_pad=0.5, h_pad=5.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tk43PP//c8Pf3Ny5evOhwTMOGDY1FixYZhmEYMTExxsiRIx36o6OjjVatWtn3r/295ufnG5UrVzY+/PBDe/+///1vw9fX1xg3bpxDXH379nU475AhQ4xnnnnGoW3r1q1GpUqVjAsXLhjfffedYbFYjB9//NFhTLdu3YwpU6YYhmEYGzduNCQZVatWddhOnjxp7zt9+rT92MzMTEOSkZ2dbRiGYf/9fPfdd/Yxb7/9thEYGGjfr1OnjjF16lTjRpy9f6ZNm+bwO6tTp44xa9YshzFt27Y1Ro0aZRjGf94P77zzjr3/m2++MSQZBw4cuOG1AQC4Vvv27Y2kpCTDMK78HVCzZk0jPT3dMAzDmDx5shEREeEwfurUqQ658la5GQAAXLFz586b1hLmz59vSDKOHz/udFxAQICxbNkywzBuXB+42b3ohg0bDC8vLyMnJ8fedvUe8uuvvzYM48p9qa+vr5Gfn28f07NnT6N+/fpGUVGRva1p06ZGYmKiYRhGqeoGAAzDu/xL/4DnMgxDFovFad9HH32kpKQkfffddzp37pwKCwvl7+/vMCY0NFT16tWz78fExKi4uFgHDx6Ul5eX/YEtV5dgkaTCwkIFBATcMKb/+7//u+Vxw4cPV48ePdS0aVP16tVLjz32mGJjYx3OM3nyZL3yyisqKCiQj4+PXnzxRT377LOaN2+ezp07V2Lt8gsXLuj777+XJB04cEAjR4506I+JidHGjRudxvyvf/1Lly9f1sMPP2xvCwgIUNOmTUuMjYqKctjPyMjQd999p/fff9/eZhiGiouLlZ2drX/84x8yDENNmjRxOK6goKDEa9i6dav8/Pzs+9WrV3carzO+vr5q2LChfT84OFgnTpyQJJ04cULHjh1Tt27dSn2+6+Xn5+vYsWPq0KGDQ3uHDh1KfL2vZcuWDnFcjeGBBx644+sDAO4NBw8e1Ndff22fReft7a2BAwdq6dKl6t69uw4ePKi2bds6HHNt/pZunZubNWtm/gsBAKACMP7/TPgb1R1u5Vb3ogcOHFBISIhCQkLsbQ8++KDuu+8+HThwwJ7z69ev73CvHBgYKC8vL1WqVMmh7eo9cEZGxi3rBgAkCvHAbThw4IB9HbZr7dy5U7/5zW80ffp09ezZUwEBAVq1apXmzZt30/NdTa4Wi0XFxcWSriwzEx0d7TDOy8vrhucozXEPPfSQsrOzlZaWps8++0xPPPGEunfvro8++sg+9sUXX9Tw4cPl6+ur4OBge2zFxcUKDg52usb6nT589UZ/XBhOvn5XtWpVh/3i4mI9++yzGjt2bImxoaGh2rt3r7y8vJSRkVHi91atWjWH/fDw8BKv4eofFtfG4myt/8qVKzvsWywW+zHXf8Xwbjj7HV3fdm0s1/53AwDgVpYsWaLCwkLVrVvX3mYYhipXrqzTp087zTvX5+tb5WYAAHBFo0aNZLFYtH//fodnqF317bffqnr16qpZs6bDPeZVzu5Nr3Wre9EbTS68vt3Z/a6ztqv3nWbUDYCKiEI8UEpffPGF9u3bpwkTJpTo27Ztm8LCwhzWTD1y5EiJcTk5OTp27Jjq1KkjSdqxY4cqVaqkJk2aKDAwUHXr1tW//vUvDR482GkMPj4+kqSioiJ7W2mOkyR/f38NHDhQAwcO1K9+9Sv16tVLp06d0v333y9Jqlmzpn3duGs99NBDysvLk7e39w3XlW/WrJl27typoUOH2tuurhXnTMOGDVW5cmV9/fXX9k/i8/PzdejQIXXu3PmGx12N55tvvnEaq3TlQTVFRUU6ceKEOnbseNNzOVOrVi1JUm5urn2GfFZW1m2dw8/PT/Xr19fnn3+url27Oh1TuXJlh/+O1/P391edOnX05ZdfqlOnTvb27du3l5iJCADAnSgsLNS7776refPmlfim3IABA/T+++/rgQceUGpqqkPf7t27HfZvlZsBAMAVNWrUUI8ePZScnKwJEyY4FM7z8vL0/vvva+jQobJYLKpVq5Zyc3Pt/YcOHdL58+ft+87qA7e6F33wwQeVk5Ojo0eP2u/F9+/frzNnztzVN9hKUzcAQCEecKqgoEB5eXkqKirS8ePHtX79eiUmJuqxxx5zKDZf1ahRI+Xk5GjVqlVq27at/va3v2nt2rUlxtlsNg0bNkxz585Vfn6+xo4dqyeeeEJBQUGSrjydfOzYsfL391dcXJwKCgq0e/dunT59WvHx8apdu7aqVKmi9evXq169erLZbAoICLjlca+//rqCg4PVunVrVapUSR9++KGCgoJK9cl09+7dFRMTo759+2rOnDlq2rSpjh07ptTUVPXt21dRUVEaN26chg0bpqioKP3iF7/Q+++/r2+++UYNGjRwek4/Pz8NGzZML774ou6//37Vrl1b06ZNU6VKlW75FbzJkyerXbt2Gj16tJ5++mlVrVpVBw4cUHp6uv77v/9bTZo00eDBgzV06FDNmzdPbdq00cmTJ/XFF1+oRYsW6t27903P36hRI4WEhCghIUEzZ87UoUOHbvnNBmcSEhI0cuRI1a5dW3FxcTp79qy2bdum559/XpLsfxx16NBBVqvV6bI4L774oqZNm6aGDRuqdevWWrZsmbKyshy++g8AwJ363//9X50+fVojRowosQzer371Ky1ZskRr1qzR/PnzNXnyZI0YMUJZWVlKSUmR9J9vYd0qNwMAgP9466231L59e/Xs2VMzZ85UeHi4vvnmG7344ouqW7euZs2aJUl65JFH9NZbb6ldu3YqLi7W5MmTHWal36w+cKN70e7du6tly5YaPHiwkpKSVFhYqFGjRqlz584lloW9HaWpGwCQKt16CHDvWb9+vYKDg1W/fn316tVLGzdu1Jtvvqm//vWvTpeJ6dOnjyZMmKAxY8aodevW2r59u1599dUS4xo1aqT+/furd+/eio2NVUREhJKTk+39Tz31lN555x2lpKSoRYsW6ty5s1JSUuzL4Xh7e+vNN9/UokWLVKdOHfXp06dUx1WrVk1z5sxRVFSU2rZtq8OHDys1NdVhfbcbsVgsSk1NVadOnfT73/9eTZo00W9+8xsdPnxYgYGBkqSBAwfqv/7rvzR58mRFRkbqyJEjeu6552563vnz5ysmJkaPPfaYunfvrg4dOqhZs2ay2Ww3Pa5ly5bavHmzDh06pI4dO6pNmzZ69dVX7WujS9KyZcs0dOhQvfDCC2ratKl++ctf6quvvnJYB+9GKleurJUrV+rbb79Vq1atNGfOHM2cOfOWx11v2LBhSkpKUnJyspo3b67HHntMhw4dsvfPmzdP6enpCgkJUZs2bZyeY+zYsXrhhRf0wgsvqEWLFlq/fr3WrVunxo0b33Y8AABcb8mSJerevbvTZ9EMGDBAWVlZOn36tD766COtWbNGLVu21IIFC+zfALRarZJKl5sBAMAVjRs31u7du9WwYUMNHDhQDRs21DPPPKOuXbtqx44d9m+tz5s3TyEhIerUqZMGDRqkiRMnytfX136eG9UHbnYvarFY9Je//EXVq1dXp06d1L17dzVo0ECrV6++q9dUmroBAMliOFuUGQDK2c8//6y6detq3rx5GjFihKvDAQAANzBr1iwtXLhQR48edXUoAAAAgMdgaRoALpGZmalvv/1WDz/8sM6cOaMZM2ZIkv1TfAAA4B6Sk5PVtm1b1ahRQ9u2bdOf/vQnjRkzxtVhAQAAAB6FQjwAl5k7d64OHjwoHx8fRUZGauvWrapZs6arwwIAANc4dOiQZs6cqVOnTik0NFQvvPCCpkyZ4uqwAAAAAI/C0jQAAAAAAAAAAJiIh7UCAAAAAAAAAGAiCvEAAAAAAAAAAJiIQjwAAAAAAAAAACaiEA8AAAAAAAAAgIkoxAMAAAAAAAAAYCIK8QAAAAAAAAAAmIhCPAAAAAAAAAAAJqIQDwAAAAAAAACAif4ftTp/oB5IyzcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1500x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot to analyze the distribution of data\n",
    "fig, ax = plt.subplots(ncols=3, nrows=3, figsize=(15,12))\n",
    "index = 0\n",
    "ax = ax.flatten()\n",
    "\n",
    "for col, value in pima.items():\n",
    "    col_dist = sns.boxplot(value, ax=ax[index])\n",
    "    col_dist.set_xlabel(col,fontsize=10)\n",
    "    col_dist.set_ylabel('density',fontsize=10)\n",
    "    index += 1\n",
    "plt.tight_layout(pad=0.5, w_pad=0.7, h_pad=5.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some outliers analyzed through the distribution of data in the histograms and boxplots.\n",
    "\n",
    "They following code has removed the outliers before to proceed with the ML model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IQR - Interquartile Range\n",
    "The lower quartile corresponds with the 25th percentile and the upper quartile corresponds with the 75th percentile, so IQR = Q3 −  Q1.\n",
    "\n",
    "The IQR is an example of a trimmed estimator, defined as the 25% trimmed range, which enhances the accuracy of dataset statistics by dropping lower contribution, outlying points.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = pima.quantile(0.25)\n",
    "Q3 = pima.quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   5.0000\n",
       "Glucose                      41.2500\n",
       "BloodPressure                18.0000\n",
       "SkinThickness                32.0000\n",
       "Insulin                     127.2500\n",
       "BMI                           9.3000\n",
       "DiabetesPedigreeFunction      0.3825\n",
       "Age                          17.0000\n",
       "Outcome                       1.0000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR = Q3 - Q1\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interquartile range is often used to find outliers in data. \n",
    "Outliers here are defined as observations that fall below Q1 − 1.5 IQR or above Q3 + 1.5 IQR. \n",
    "In a boxplot, the highest and lowest occurring value within this limit are indicated by whiskers of the box (frequently with an additional bar at the end of the whisker) and any outliers as individual points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(639, 9)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outlier removal\n",
    "pima = pima[~((pima < (Q1 - 1.5 * IQR)) | (pima > (Q3 + 1.5 * IQR))).any(axis = 1)]\n",
    "pima.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    439\n",
       "1    200\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = pima[\"Outcome\"].value_counts()\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data distribution\n",
    "fig, ax = plt.subplots(ncols=3, nrows=3, figsize=(15,12))\n",
    "index = 0\n",
    "ax = ax.flatten()\n",
    "\n",
    "for col, value in pima.items():\n",
    "    col_dist = sns.boxplot(value, ax=ax[index])\n",
    "    col_dist.set_xlabel(col,fontsize=10)\n",
    "    col_dist.set_ylabel('density',fontsize=10)\n",
    "    index += 1\n",
    "plt.tight_layout(pad=0.5, w_pad=0.7, h_pad=5.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram to analyze the distribution of data\n",
    "fig, ax = plt.subplots(ncols=3, nrows=3, figsize=(15,12))\n",
    "index = 0\n",
    "ax = ax.flatten()\n",
    "\n",
    "for col, value in pima.items():\n",
    "    col_dist = sns.histplot(value, ax=ax[index], kde=True, stat=\"density\", linewidth=0)\n",
    "    col_dist.set_xlabel(col,fontsize=10)\n",
    "    col_dist.set_ylabel('density',fontsize=10)\n",
    "    index += 1\n",
    "plt.tight_layout(pad=0.8, w_pad=0.5, h_pad=5.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Logistic Regression ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define x and y\n",
    "feature_cols =['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']\n",
    "x=pima[feature_cols]\n",
    "y=pima.Outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split x and y into training (70%) and testing (30%) sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize features\n",
    "Standardize features by removing the mean and scaling to unit variance.\n",
    "\n",
    "The standard score of a sample x is calculated as: z = (x - u) / s, where u is the mean of the training samples or zero if with_mean=False, and s is the standard deviation of the training samples or one if with_std=False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = StandardScaler()\n",
    "x_train = scale.fit_transform(x_train)\n",
    "x_test = scale.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=3000)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Logistic regression model fit on the training set\n",
    "logreg = LogisticRegression(solver='lbfgs', max_iter=3000)\n",
    "logreg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using the trained model to predict the outcome for samples in x_test.\n",
    "y_pred = logreg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Return the probability estimates.\n",
    "y_score = logreg.predict_proba(x_test)[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ML Model Assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Precision: Percentage of correct positive predictions relative to total positive predictions.\n",
    "2. Recall: Percentage of correct positive predictions relative to total actual positives.\n",
    "3. F1 Score: A weighted harmonic mean of precision and recall. The closer to 1, the better the model.\n",
    "F1 Score: 2 * (Precision * Recall) / (Precision + Recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.93      0.85       127\n",
      "           1       0.78      0.48      0.59        65\n",
      "\n",
      "    accuracy                           0.78       192\n",
      "   macro avg       0.78      0.70      0.72       192\n",
      "weighted avg       0.78      0.78      0.76       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Comparing actual result and predicted result\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Precision:** Out of all the patients that the model predicted would get diabetes, 78% actually did. \n",
    "2. **Recall:** Out of all the patients that actually did get diabetes, the model predicted this outcome correctly for 48% of those patients. \n",
    "3. **F1 Score:** 0.59 - Since this value is close to 1, it tells us that the model does a good job of predicting whether or not patients will get diabetes.\n",
    "2 * (Precision * Recall) / (Precision + Recall) ***** 2 * (.78 * .48) / (.78 + .48)\n",
    "\n",
    "4. **Support:** These values is regarding how many patients belonged to each class in the test dataset. Among the patientis in the test dataset, 127 did not get diabetes and 65 did get diabetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4732423621500228"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The lower the RMS value, the better. O means the model is perfect.\n",
    "rms = mean_squared_error(y_test, y_pred, squared=False)\n",
    "rms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ROC curve shows the trade-off between sensitivity (or True Positve Rate) and specificity (1 – False Positive Rate).\n",
    "\n",
    "Classifiers that give curves closer to the top-left corner indicate a better performance\n",
    "\n",
    "ROC/AUC does not require to set a classification threshold and it's still useful when there is high class imbalance\n",
    "\n",
    "- ROC curve can help you to choose a threshold that balances sensitivity and specificity in a way that makes sense for your particular context\n",
    "\n",
    "- You can't actually see the thresholds used to generate the curve on the ROC curve itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresh = roc_curve(y_test, y_score, pos_label=logreg.classes_[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7030284675953968\n"
     ]
    }
   ],
   "source": [
    "#AUC is the percentage of the ROC plot that is underneath the curve:\n",
    "# IMPORTANT: first argument is true values, second argument is predicted probabilities\n",
    "print(metrics.roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(fpr, tpr, linewidth=1, marker='.', label='Logistic')\n",
    "plt.plot([0,1], [0,1], linestyle='--', label='No Skill')\n",
    "plt.rcParams['font.size'] = 8\n",
    "plt.title('ROC curve')\n",
    "plt.xlabel('Specificity (FPR)')\n",
    "plt.ylabel('Sensitivity (TPR)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy\n",
    "Percentage of correct predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic regression model is 0.7760416666666666\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of Logistic regression model is {}'.format(accuracy_score(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Null accuracy\n",
    "Null accuracy refers to the accuracy that could be achieved by always predicting the most frequent class in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the test set, 66% (127) of patients did not have diabetes, while 34% did.\n",
    "In this case, the null accuracy would be 66%, because if we always predicted \"not diabetes,\" we would be correct 66% of the time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.661458\n",
       "Name: Outcome, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts().head(1) / len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the null accuracy is less than model accuracy, it indicates a good result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing the true and predicted response values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       " array([0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
       "        0, 0, 0]))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the 25 first true and predict responses\n",
    "print ('True:'), y_test.values[0:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pred:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       " array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "        0, 0, 1]))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print ('Pred:'), y_pred[0:25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Confusion matrix allows to calculate a variety of metrics. It's a useful for multi-class problems (more than two response classes)\n",
    "\n",
    "- Every observation in the testing set is represented in exactly one box\n",
    "- It's 2x2 matrix because there are 2 responses classes\n",
    "- The format shown here is not universal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic Terminology\n",
    "- TP: correctly predict that they have diabetes\n",
    "- TN: correctly predict that they do not have diabetes\n",
    "- FP: incorrectly predict that they do have diabetes\n",
    "- FN: incorrectly predict that they do not have diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[118,   9],\n",
       "       [ 34,  31]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#IMPORTANT: First argument is true values, second argument is predict values\n",
    "metrics.confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graphic visualization\n",
    "cm = confusion_matrix(y_test, y_pred, labels=logreg.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=logreg.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- TN: 118 patients without diabetes were **correctly** predicted as no diabetics\n",
    "- FP: 9 patients without diabetes were incorrectly predicted as diabetics\n",
    "- FN: 34 patients with diabetes were incorrectly predicted as no diabetics\n",
    "- TP: 31 patients with diabetes were **correctly** predicted as diabetics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save confusion matrix and slice into four pieces\n",
    "confusion = metrics.confusion_matrix(y_test, y_pred)\n",
    "TN = confusion[0,0]\n",
    "FP = confusion[0,1]\n",
    "FN = confusion[1,0]\n",
    "TP = confusion[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[118,   9],\n",
       "       [ 34,  31]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metrics computed from a confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7760416666666666\n"
     ]
    }
   ],
   "source": [
    "#How often the classifier is correct?\n",
    "print('Accuracy: {}'.format((TP + TN) / (TP + TN + FP + FN)))\n",
    "#print('Accuracy: {}'.format(metrics.accuracy_score(y_test, y_pred_class)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### k-Fold Cross-Validation\n",
    "The k-fold cross-validation procedure divides a limited dataset into k non-overlapping folds. Each of the k folds is given an opportunity to be used as a held back test set, whilst all other folds collectively are used as a training dataset. A total of k models are fit and evaluated on the k hold-out test sets and the mean performance is reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.784 (0.033)\n"
     ]
    }
   ],
   "source": [
    "#This will give the overall accuracy of your model .\n",
    "kf = KFold(n_splits=10, random_state=1, shuffle=True)\n",
    "cv_result = cross_val_score(logreg, x, y, cv=kf, scoring='accuracy', n_jobs=-1)\n",
    "print('Accuracy: %.3f (%.3f)' % (mean(cv_result), std(cv_result)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: 0.22395833333333334\n"
     ]
    }
   ],
   "source": [
    "#How often the classifier is incorrect?\n",
    "print('Error: {}'.format((FP + FN)/(TP + TN + FP + FN)))\n",
    "#print('Error: {}'.format(1-metrics.accuracy_score(y_test, y_pred_class)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity (TPR): 0.47692307692307695\n"
     ]
    }
   ],
   "source": [
    "#When the actual value is positive, how often is the prediction correct?\n",
    "print('Sensitivity (TPR): {}'.format(TP / (TP + FN)))\n",
    "#metrics.recall_score(y_test, y_pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Specificity (FPR): 0.9291338582677166\n"
     ]
    }
   ],
   "source": [
    "#When the actual value is negative, how often is the prediction correct?\n",
    "print('Specificity (FPR): {}'.format(TN / (TN + FP))) \n",
    "#recall_score(y_test, y_pred_class, pos_label=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.775\n"
     ]
    }
   ],
   "source": [
    "#When a positive value is predicted, how often is the prediction correct?\n",
    "print('Precision: {}'.format(TP / (TP + FP))) \n",
    "#metrics.precision_score(y_test, y_pred_class)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The choice of metrics depends on the business objective.\n",
    "\n",
    "For this project, sensibility (False Negative) is the metric most important, since predicting diabetics as no diabetics is the worst expected error. This may imply no further investigations and consequently no treatment of the disease.\n",
    "\n",
    "So the better result is to have a Sensitivity (The correct prediction for positive values) result higher than a Specificity (The correct prediction for negative values) result.\n",
    "\n",
    "The error in predicting a healthy patient as a diabetic patient is more acceptable than the opposite."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Adjusting the classification threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the first 10 predicted responses\n",
    "logreg.predict(x_test)[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.83358342, 0.16641658],\n",
       "       [0.80138151, 0.19861849],\n",
       "       [0.95231282, 0.04768718],\n",
       "       [0.87348851, 0.12651149],\n",
       "       [0.67158939, 0.32841061],\n",
       "       [0.38292382, 0.61707618],\n",
       "       [0.93226643, 0.06773357],\n",
       "       [0.5219636 , 0.4780364 ],\n",
       "       [0.92950252, 0.07049748],\n",
       "       [0.69118097, 0.30881903]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the first 10 predicted probabilities of class membership\n",
    "logreg.predict_proba(x_test)[0:10, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.16641658, 0.19861849, 0.04768718, 0.12651149, 0.32841061,\n",
       "       0.61707618, 0.06773357, 0.4780364 , 0.07049748, 0.30881903])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the first 10 predicted probabilities for class 1\n",
    "logreg.predict_proba(x_test)[0:10, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.histplot(data=y_score, bins=8)\n",
    "plt.xlabel(\"Predicted probability of diabetes\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Histogram of predicted probabilities\") \n",
    "plt.xlim(0,1)\n",
    "plt.rcParams['font.size']=10\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding the best threshold for predicting diabetes and to increase the sensitivity of the classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Threshold of 0.5 is used by default (for binary problems) to convert predicted possibilities into class predictions\n",
    "- Threshold can be adjusted to increase sensitivity or specificity\n",
    "- Sensitivity and specificity have an inverse relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate roc curves\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Threshold=0.242365, G-Mean=0.796\n"
     ]
    }
   ],
   "source": [
    "# calculate the g-mean for each threshold\n",
    "gmeans = sqrt(tpr * (1-fpr))\n",
    "# locate the index of the largest g-mean\n",
    "ix = argmax(gmeans)\n",
    "print('Best Threshold=%f, G-Mean=%.3f' % (thresholds[ix], gmeans[ix]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Threshold=0.242365\n"
     ]
    }
   ],
   "source": [
    "# get the best threshold\n",
    "J = tpr - fpr\n",
    "ix = argmax(J)\n",
    "best_thresh = thresholds[ix]\n",
    "print('Best Threshold=%f' % (best_thresh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a function that accepts a threshold and prints sensitivity and specificity\n",
    "def evaluate_threshold(threshold):\n",
    "    print('Sensitivity:', tpr[thresholds > threshold][-1])\n",
    "    print('Specificity:', 1 - fpr[thresholds > threshold][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity: 0.47692307692307695\n",
      "Specificity: 0.9291338582677166\n"
     ]
    }
   ],
   "source": [
    "evaluate_threshold(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity: 0.7230769230769231\n",
      "Specificity: 0.7795275590551181\n"
     ]
    }
   ],
   "source": [
    "evaluate_threshold(0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity: 0.8461538461538461\n",
      "Specificity: 0.7480314960629921\n"
     ]
    }
   ],
   "source": [
    "evaluate_threshold(0.242365)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best sensitivity with a good specificity was achieved by the calculated threshold of 0.242365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict diabetes if the predicted probabilities is greater than 0.242365\n",
    "y_pred_2 = preprocessing.binarize([y_score], threshold=0.242365)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the first 10 predicted probabilites\n",
    "y_pred[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 1., 1., 0., 1., 0., 1.])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the first 10 predicted classes with the lower threshold\n",
    "y_pred_2[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[118,   9],\n",
       "       [ 34,  31]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#previous confusion matrix (default threshold of 0.5)\n",
    "confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now confusion matrix (threshold of 0.3)\n",
    "confusion_2 = metrics.confusion_matrix(y_test, y_pred_2)\n",
    "TN = confusion_2[0,0]\n",
    "FP = confusion_2[0,1]\n",
    "FN = confusion_2[1,0]\n",
    "TP = confusion_2[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[95, 32],\n",
       "       [10, 55]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity (TPR): 0.8461538461538461\n"
     ]
    }
   ],
   "source": [
    "#sensitivity has increased (used to be 0.7384615384615385)\n",
    "print('Sensitivity (TPR): {}'.format(TP / (TP + FN)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Specificity (FPR): 0.7480314960629921\n"
     ]
    }
   ],
   "source": [
    "#sensitivity has increased (used to be 0.7795275590551181)\n",
    "print('Specificity (FPR): {}'.format(TN / (TN + FP))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7970926711084192\n"
     ]
    }
   ],
   "source": [
    "#AUC is the percentage of the ROC plot that is underneath the curve:\n",
    "print(metrics.roc_auc_score(y_test, y_pred_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a threshold of 0.242365, the sensitivity has increased from 0.48 to 0.85, and the specificity has decreased from 0.93 to 0.75. Despite the decrease in specificity, for this project, the most important metric is sensitivity, once we want to correctly predict the patients with diabetes, the correct prediction for positive values.\n",
    "\n",
    "The AUC has also increased after threshold adjustment, from 0.70 to 0.79."
   ]
  }
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