diabetesKNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "05e76c21",
   "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>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",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Outcome == 1 Diabetes = True\n",
    "# Outcome == 0 Diabetes = False\n",
    "\n",
    "data = pd.read_csv('diabetes.csv')\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b2f13041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "person_diabetes = data[data.Outcome == 1]\n",
    "person_healthy = data[data.Outcome == 0]\n",
    "\n",
    "#Let's we draw to our graph by looking at glucose for now.\n",
    "#Our model will guess using other points at the last of our program.\n",
    "\n",
    "plt.scatter(person_healthy.Age, person_healthy.Glucose, color ='green', label = 'Healthy', alpha = 0.35)\n",
    "plt.scatter(person_diabetes.Age, person_diabetes.Glucose, color = 'red', label = 'Diabetes', alpha = 0.35)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Glucose')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "405d4443",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw datas before from the normalization:\n",
      "\n",
      "   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  \n",
      "0                     0.627   50  \n",
      "1                     0.351   31  \n",
      "2                     0.672   32  \n",
      "3                     0.167   21  \n",
      "4                     2.288   33  \n",
      "\n",
      "\n",
      "Our datas training to AI after normalization: \n",
      "\n",
      "   Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
      "0     0.352941  0.743719       0.590164       0.353535  0.000000  0.500745   \n",
      "1     0.058824  0.427136       0.540984       0.292929  0.000000  0.396423   \n",
      "2     0.470588  0.919598       0.524590       0.000000  0.000000  0.347243   \n",
      "3     0.058824  0.447236       0.540984       0.232323  0.111111  0.418778   \n",
      "4     0.000000  0.688442       0.327869       0.353535  0.198582  0.642325   \n",
      "\n",
      "   DiabetesPedigreeFunction       Age  \n",
      "0                  0.234415  0.483333  \n",
      "1                  0.116567  0.166667  \n",
      "2                  0.253629  0.183333  \n",
      "3                  0.038002  0.000000  \n",
      "4                  0.943638  0.200000  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Public\\anaconda\\envs\\tf\\lib\\site-packages\\numpy\\core\\fromnumeric.py:84: FutureWarning: In a future version, DataFrame.min(axis=None) will return a scalar min over the entire DataFrame. To retain the old behavior, use 'frame.min(axis=0)' or just 'frame.min()'\n",
      "  return reduction(axis=axis, out=out, **passkwargs)\n",
      "C:\\Users\\Public\\anaconda\\envs\\tf\\lib\\site-packages\\numpy\\core\\fromnumeric.py:84: FutureWarning: In a future version, DataFrame.max(axis=None) will return a scalar max over the entire DataFrame. To retain the old behavior, use 'frame.max(axis=0)' or just 'frame.max()'\n",
      "  return reduction(axis=axis, out=out, **passkwargs)\n"
     ]
    }
   ],
   "source": [
    "# Let's determine the ordinates and abscissas\n",
    "y = data.Outcome.values\n",
    "x_raw_data = data.drop([\"Outcome\"], axis=1)\n",
    "#We remove the outcome column(dependent variable) and leave only the independent variables.\n",
    "#Because the KNN algorithms will group in the abscissas.\n",
    "\n",
    "#To make normalization - We update all values into the x_raw_data as 0 and 1.\n",
    "#If we don't make normalization like this, our KNN algorithm is failed.\n",
    "\n",
    "x= (x_raw_data - np.min(x_raw_data))/(np.max(x_raw_data)-np.min(x_raw_data))\n",
    "\n",
    "#Before\n",
    "\n",
    "print('Raw datas before from the normalization:\\n')\n",
    "print(x_raw_data.head())\n",
    "\n",
    "#After\n",
    "print(\"\\n\\nOur datas training to AI after normalization: \\n\")\n",
    "print(x.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d076e275",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The confirmation test result of our test data for K=3 0.875\n",
      "1   Accuracy Rate: % 75.0\n",
      "2   Accuracy Rate: % 87.5\n",
      "3   Accuracy Rate: % 87.5\n",
      "4   Accuracy Rate: % 100.0\n",
      "5   Accuracy Rate: % 75.0\n",
      "6   Accuracy Rate: % 87.5\n",
      "7   Accuracy Rate: % 75.0\n",
      "8   Accuracy Rate: % 87.5\n",
      "9   Accuracy Rate: % 87.5\n",
      "10   Accuracy Rate: % 87.5\n"
     ]
    }
   ],
   "source": [
    "#We separate our train data with our test data.\n",
    "#Our train data to be used for the system separation the healthy person with diabetes person.\n",
    "#Our test data to be used for looking at the machine learning model succesbility's.\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.01, random_state=1)\n",
    "\n",
    "#To create the KNN model.\n",
    "knn = KNeighborsClassifier(n_neighbors =3) #n_neighbors  = k\n",
    "knn.fit(x_train, y_train)\n",
    "prediction = knn.predict(x_test)\n",
    "print(\"The confirmation test result of our test data for K=3\", knn.score(x_test, y_test))\n",
    "\n",
    "#So how much should the \"k\" be?\n",
    "#Let's to determine the \"k\".\n",
    "\n",
    "counter = 1\n",
    "for k in range(1,11):\n",
    "    knn_new = KNeighborsClassifier(n_neighbors = k)\n",
    "    knn_new.fit(x_train, y_train)\n",
    "    print(counter, \" \", \"Accuracy Rate: %\", knn_new.score(x_test,y_test)*100)\n",
    "    counter +=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e5b3dea3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Public\\anaconda\\envs\\tf\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\Public\\anaconda\\envs\\tf\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To guess a new patient.\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#To make normalization - We used MİnMax Scaler to make faster normalization.\n",
    "sc = MinMaxScaler()\n",
    "sc.fit_transform(x_raw_data)\n",
    "\n",
    "new_prediction = knn.predict(sc.transform(np.array([[6,148,72,35,0,33.6,0.627,50]])))\n",
    "new_prediction[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d955890e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}