Diabetes Classification Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def euclid_dist(x1,x2):\n",
    "    return np.sqrt(sum((x1-x2)**2))\n",
    "\n",
    "def knn(X,Y,query_point,k=5):\n",
    "    \n",
    "    dist_values=[]\n",
    "    m=X.shape[0]\n",
    "    \n",
    "    for i in range(m):\n",
    "        d=euclid_dist(query_point,X[i])\n",
    "        dist_values.append((d,Y[i]))#(distance,label)\n",
    "    dist_values=sorted(dist_values)\n",
    "    #Nearest /First K points\n",
    "    dist_values=dist_values[:24]\n",
    "    vals=np.array(dist_values)\n",
    "    new_vals=np.unique(vals[:,1],return_counts=True)\n",
    "    print(new_vals[0][new_vals[1].argmax()]) #new_vals[1].argmax() will give the index of maximum frquency oflabel \n",
    "    return new_vals[0][new_vals[1].argmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\lmd\\Documents\\Data-Science\\Training Data\\Diabetes_XTrain.csv\")\n",
    "dfy=pd.read_csv(r\"C:\\Users\\lmd\\Documents\\Data-Science\\Training Data\\Diabetes_YTrain.csv\")\n",
    "dfxtest=pd.read_csv(r\"C:\\Users\\lmd\\Documents\\Data-Science\\Training Data\\Diabetes_Xtest.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>168</td>\n",
       "      <td>88</td>\n",
       "      <td>42</td>\n",
       "      <td>321</td>\n",
       "      <td>38.2</td>\n",
       "      <td>0.787</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>110</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.237</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>147</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.257</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>20</td>\n",
       "      <td>90</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.867</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>129</td>\n",
       "      <td>86</td>\n",
       "      <td>20</td>\n",
       "      <td>270</td>\n",
       "      <td>35.1</td>\n",
       "      <td>0.231</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            7      168             88             42      321  38.2   \n",
       "1            8      110             76              0        0  27.8   \n",
       "2            7      147             76              0        0  39.4   \n",
       "3            2      100             66             20       90  32.9   \n",
       "4            4      129             86             20      270  35.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  \n",
       "0                     0.787   40  \n",
       "1                     0.237   58  \n",
       "2                     0.257   43  \n",
       "3                     0.867   28  \n",
       "4                     0.231   23  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=5)\n",
    "# print(dfxtest.head(n=5))\n"
   ]
  },
  {
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>90</td>\n",
       "      <td>26</td>\n",
       "      <td>90</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.314</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>93</td>\n",
       "      <td>64</td>\n",
       "      <td>32</td>\n",
       "      <td>160</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.674</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>114</td>\n",
       "      <td>68</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>28.7</td>\n",
       "      <td>0.092</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.8</td>\n",
       "      <td>0.180</td>\n",
       "      <td>41</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>60</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.527</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            0      180             90             26       90  36.5   \n",
       "1            2       93             64             32      160  38.0   \n",
       "2            2      114             68             22        0  28.7   \n",
       "3           13       76             60              0        0  32.8   \n",
       "4            1       80             74             11       60  30.0   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  \n",
       "0                     0.314   35  \n",
       "1                     0.674   23  \n",
       "2                     0.092   25  \n",
       "3                     0.180   41  \n",
       "4                     0.527   22  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfxtest.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
      "       'BMI', 'DiabetesPedigreeFunction', 'Age'],\n",
      "      dtype='object')\n",
      "(576, 8)\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.values\n",
    "X_test=dfxtest.values\n",
    "Y=dfy.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  7.    168.     88.     42.    321.     38.2     0.787  40.   ]\n",
      "[  0.    180.     90.     26.     90.     36.5     0.314  35.   ]\n"
     ]
    }
   ],
   "source": [
    "print(X[0])\n",
    "print(X_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=Y.reshape((-1,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(576,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "192\n"
     ]
    }
   ],
   "source": [
    "m=X_test.shape[0]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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   "source": [
    "Output=[]\n",
    "for i in range(m):\n",
    "    Output.append(knn(X,Y,X_test[i],k=5))"
   ]
  },
  {
   "cell_type": "code",
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     "execution_count": 14,
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   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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   "execution_count": 16,
   "metadata": {},
   "outputs": [],
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    "my_df = pd.DataFrame({'Output':Output})"
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   "execution_count": 17,
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       "     Output\n",
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       "\n",
       "[192 rows x 1 columns]"
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     "execution_count": 17,
     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_df.to_csv('diab.csv', index=False)"
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Output\n",
       "0       0.0\n",
       "1       0.0\n",
       "2       0.0\n",
       "3       0.0\n",
       "4       0.0\n",
       "5       0.0\n",
       "6       1.0\n",
       "7       0.0\n",
       "8       0.0\n",
       "9       0.0\n",
       "10      1.0\n",
       "11      0.0\n",
       "12      1.0\n",
       "13      0.0\n",
       "14      0.0\n",
       "15      0.0\n",
       "16      0.0\n",
       "17      1.0\n",
       "18      1.0\n",
       "19      1.0\n",
       "20      0.0\n",
       "21      0.0\n",
       "22      0.0\n",
       "23      0.0\n",
       "24      0.0\n",
       "25      0.0\n",
       "26      0.0\n",
       "27      0.0\n",
       "28      0.0\n",
       "29      0.0\n",
       "..      ...\n",
       "162     0.0\n",
       "163     0.0\n",
       "164     0.0\n",
       "165     0.0\n",
       "166     0.0\n",
       "167     0.0\n",
       "168     0.0\n",
       "169     0.0\n",
       "170     0.0\n",
       "171     1.0\n",
       "172     0.0\n",
       "173     0.0\n",
       "174     0.0\n",
       "175     0.0\n",
       "176     0.0\n",
       "177     0.0\n",
       "178     1.0\n",
       "179     1.0\n",
       "180     0.0\n",
       "181     0.0\n",
       "182     1.0\n",
       "183     0.0\n",
       "184     1.0\n",
       "185     0.0\n",
       "186     0.0\n",
       "187     0.0\n",
       "188     0.0\n",
       "189     1.0\n",
       "190     0.0\n",
       "191     0.0\n",
       "\n",
       "[192 rows x 1 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('diab.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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