Predict whether a person will have diabetes or not.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Predict whether a person will be diagnosed with diabetes or not - using KNN Algorithm</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Import the Dataset </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/diabetes.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Remove zero values and Import the mean values</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "not_zero = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "\n",
    "for column in not_zero:\n",
    "    data[column] = data[column].replace(0,np.NaN)\n",
    "    mean = int(data[column].mean(skipna=True))\n",
    "    data[column] = data[column].replace(np.NaN,mean)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x214a6da9cc0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.Insulin,data.Outcome, alpha='0.8', color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Split the Dataset</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, 0:8]\n",
    "y = data['Outcome']\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Choose a value of 'K' </h3><br>\n",
    "<h4><ol><li>sqrt(n), Where 'n' is total number of data points</li><br>\n",
    "    <li>Odd value of 'K' is selected to avoid confusion between two classes of data</li></ol> </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.409673645990857"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.sqrt(len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> '12'  is even number,  so we will choose odd number for near the 12.<br><br><br> K = 11 </h4><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Define the KNN - model </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='euclidean',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=11, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=11, p=2, metric='euclidean')\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Predict the Values</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Evaluate the Score</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7467532467532467"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Make a Prediction</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction=knn.predict([[6,148.0,62.0,35.0,455.0,33.6,0.627,30]])\n",
    "prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<h2>Conclusion:</h2><br>\n",
    "    \n",
    "<h3> When insulin level is high, that person will be diagnosed with diabetes. So the prediction is correct.</h3> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "<center><h1>*** THE END ***</h1></center>"
   ]
  }
 ],
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