Diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfx = pd.read_csv('Diabetes_XTrain.csv')\n",
    "dfy = pd.read_csv('Diabetes_YTrain.csv')\n",
    "test = pd.read_csv('Diabetes_Xtest.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <td>39.4</td>\n",
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       "      <td>270</td>\n",
       "      <td>35.1</td>\n",
       "      <td>0.231</td>\n",
       "      <td>23</td>\n",
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      "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  "
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "dfx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
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      "text/plain": [
       "   Outcome\n",
       "0        1\n",
       "1        0\n",
       "2        1\n",
       "3        1\n",
       "4        0"
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     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "dfy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfx.values\n",
    "Y = dfy.values\n",
    "T = test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#KNN\n",
    "def dist(x1,x2):\n",
    "    return np.sqrt(sum((x1-x2)**2))\n",
    "                   \n",
    "def knn(X,Y,queryPoint,k=17):\n",
    "    vals = []\n",
    "    m = X.shape[0]\n",
    "    for i in range(m):\n",
    "        d = dist(queryPoint,X[i])\n",
    "        vals.append((d,Y[i]))\n",
    "    vals = sorted(vals)\n",
    "    vals = vals[:k]\n",
    "    \n",
    "    vals = np.array(vals)\n",
    "    new_vals = np.unique(vals[:,1],return_counts = True)\n",
    "    \n",
    "    index = new_vals[1].argmax()\n",
    "    pred = new_vals[0][index]\n",
    "    return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "text": [
      "C:\\Users\\sapre\\AppData\\Local\\Temp/ipykernel_118320/1913385486.py:14: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  vals = np.array(vals)\n"
     ]
    }
   ],
   "source": [
    "diabetes_pred = []\n",
    "for i in range(T.shape[0]):\n",
    "    x = knn(X,Y,T[i])\n",
    "    diabetes_pred.append(x[0])"
   ]
  },
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   "execution_count": 8,
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     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "diabetes_pred"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "op = pd.DataFrame({'Outcome':np.array(diabetes_pred)})\n",
    "op.to_csv('Diabetes_Output.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sapre\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=17)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier(n_neighbors = 17)\n",
    "classifier.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = classifier.predict(T)"
   ]
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    "pred"
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