GLASS DATASET - kNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E:\\ADG\\Data_Set\\Glass\n"
     ]
    }
   ],
   "source": [
    "cd E:\\ADG\\Data_Set\\Glass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.52101</td>\n",
       "      <td>13.64</td>\n",
       "      <td>4.49</td>\n",
       "      <td>1.10</td>\n",
       "      <td>71.78</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.51761</td>\n",
       "      <td>13.89</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.36</td>\n",
       "      <td>72.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51618</td>\n",
       "      <td>13.53</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1.54</td>\n",
       "      <td>72.99</td>\n",
       "      <td>0.39</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.51766</td>\n",
       "      <td>13.21</td>\n",
       "      <td>3.69</td>\n",
       "      <td>1.29</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.51742</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.24</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.55</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RI     Na    Mg    Al     Si     K    Ca   Ba   Fe  Type\n",
       "0  1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.0  0.0     1\n",
       "1  1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.0  0.0     1\n",
       "2  1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.0  0.0     1\n",
       "3  1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.0  0.0     1\n",
       "4  1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.0  0.0     1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv('datasets_738_1370_glass.csv')\n",
    "data.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RI      False\n",
       "Na      False\n",
       "Mg      False\n",
       "Al      False\n",
       "Si      False\n",
       "K       False\n",
       "Ca      False\n",
       "Ba      False\n",
       "Fe      False\n",
       "Type    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 214 entries, 0 to 213\n",
      "Data columns (total 10 columns):\n",
      "RI      214 non-null float64\n",
      "Na      214 non-null float64\n",
      "Mg      214 non-null float64\n",
      "Al      214 non-null float64\n",
      "Si      214 non-null float64\n",
      "K       214 non-null float64\n",
      "Ca      214 non-null float64\n",
      "Ba      214 non-null float64\n",
      "Fe      214 non-null float64\n",
      "Type    214 non-null int64\n",
      "dtypes: float64(9), int64(1)\n",
      "memory usage: 16.8 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 214 entries, 0 to 213\n",
      "Data columns (total 10 columns):\n",
      "RI      214 non-null float64\n",
      "Na      214 non-null float64\n",
      "Mg      214 non-null float64\n",
      "Al      214 non-null float64\n",
      "Si      214 non-null float64\n",
      "K       214 non-null float64\n",
      "Ca      214 non-null float64\n",
      "Ba      214 non-null float64\n",
      "Fe      214 non-null float64\n",
      "Type    214 non-null category\n",
      "dtypes: category(1), float64(9)\n",
      "memory usage: 15.5 KB\n"
     ]
    }
   ],
   "source": [
    "data['Type'] = data['Type'].astype('category')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data['Type']\n",
    "X = data.drop(['Type'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "principal_components = pca.fit_transform(X)\n",
    "new_data= pd.DataFrame(data = principal_components, columns = ['principal component 1', 'principal component 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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-0.15860404182578713), (3, -1.0165947323963327, -0.26956050686613614), (3, -0.9415730391248492, -0.6967850761015811), (3, -0.9560488293732875, -0.14863328591486813), (3, -0.49548751745098246, -1.0973716989016387), (3, -0.7315466326374138, -0.6366431894931133), (3, -0.9818392955386683, -0.07001262779195147), (3, -0.7990362216862146, -0.6711054934504594), (3, -0.8067783420520451, -0.3167092895443946), (3, -0.741816129221074, -0.2735319538503735), (3, -0.31287268238855653, -1.2326741352584858), (3, -0.6480258873669066, -0.370984676327362), (3, -0.5920395536887025, -0.36161646787224766), (3, -0.46315412178385473, -0.5135668667098071), (3, -0.6679880281233463, -0.5544030422133668), (3, -0.6617165828461363, -0.8569926574601873), (5, -1.906324613208937, 2.718919725974767), (5, 1.3597267502844652, -0.42836481516481906), (5, 2.2100802409412004, -1.7834838656653076), (5, 2.4536054728390218, -1.9080451943746128), (5, 3.674871899308522, -0.2191569432425758), (5, 2.7153253190460807, 0.8404097163867982), (5, 3.5465632838590917, 0.0596675941185662), (5, 4.184486734080515, -0.6593094482324687), (5, 0.20757325204190857, 3.08124317087342), (5, 0.18421795409550856, 3.129292415895079), (5, 4.350821388837793, -0.883311035973466), (5, 1.2948847910879218, 0.03330014045843488), (5, 3.2812021054573304, -0.24318171131097813), (6, 0.6730446320631869, -0.09650029929504388), (6, 0.7707557555280595, -0.33452922496294324), (6, 0.5814885543652726, 0.3770613880313884), (6, 0.6516975357847359, 0.2862954669503297), (6, -0.21331184889124963, 2.0550706924119773), (6, 2.120620797330003, 1.0100896247151707), (6, 3.317016865378906, 0.6623015720756041), (6, 3.4785969370166065, 0.3637234844097505), (6, 0.39581445598636517, 4.624430726577156), (7, -2.7713200607938857, 2.492117425554538), (7, -2.4710572981175827, 2.359297474542779), (7, -0.5893562011716915, -0.4005359177213578), (7, 0.9342932918119423, 0.03125585772035606), (7, 0.5638511022409843, 1.4799969836833056), (7, 0.5636588245192136, 1.1130769110105259), (7, 1.946304914043332, 2.5703529369333493), (7, 2.1215801317149383, 2.2300148978448613), (7, 1.8451727136732916, 2.7360302547005726), (7, 1.8305622416205831, 2.7093516120307366), (7, 2.1492418974215557, 2.197296451112865), (7, 2.380970202809265, 1.864252060078776), (7, 2.072460779836553, 2.388981575818573), (7, 2.1668590242746384, 2.2287696947464446), (7, 1.9920331765559118, 2.562109689285427), (7, 1.6574291249606117, 2.957344165471514), (7, 1.6815058534157, 1.5085249402802487), (7, 1.6870868584717358, 2.8779236922529887), (7, 1.6840388995066145, 2.9532002947324365), (7, 1.9110001735881579, 2.624695001050925), (7, 1.8908321387574503, 2.701261607721385), (7, 1.9207814368278078, 2.663678757342388), (7, 0.41952621319078026, 4.221379577582592), (7, 2.4193136451665103, 1.9422685011986829), (7, 2.273595509628599, 2.131266082869744), (7, 1.758893875404084, 2.8905185926234407), (7, 1.7788783515762554, 2.7313329667469657), (7, 1.7958939743601405, 2.715168391254284), (7, 1.9045173441528673, 2.5790754905797524)]\n"
     ]
    }
   ],
   "source": [
    "p1 = new_data['principal component 1']\n",
    "p2 = new_data['principal component 2']\n",
    "l1 = []\n",
    "for i in range(len(p1)):\n",
    "    l1.append((Y[i], p1[i], p2[i]))\n",
    "    \n",
    "print(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "for i in l1:\n",
    "    if i[0] == 1:\n",
    "        plt.scatter(i[1], i[2], label = i[0])\n",
    "    if i[0] == 2:\n",
    "        plt.scatter(i[1], i[2], label = i[0])\n",
    "    if i[0] == 3:\n",
    "        plt.scatter(i[1], i[2], label = i[0])\n",
    "    if i[0] == 4:\n",
    "        plt.scatter(i[1], i[2], label = i[0])\n",
    "    if i[0] == 5:\n",
    "        plt.scatter(i[1], i[2], label = i[0])\n",
    "    if i[0] == 6:\n",
    "        plt.scatter(i[1], i[2], label = i[0])\n",
    "    if i[0] == 7:\n",
    "        plt.scatter(i[1], i[2], label = i[0])\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(new_data, Y, test_size = 0.3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 1, 1, 2, 5, 2, 1, 1, 2, 2, 1, 3, 1, 2, 2, 7, 1, 2, 2, 3, 6, 1, 7, 7, 1, 1, 7, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 7, 2, 5, 2, 3, 2, 2, 1, 2, 3, 2, 2, 5, 7, 1, 2, 1, 1, 1, 3, 2, 1, 2, 1, 2, 1]\n"
     ]
    }
   ],
   "source": [
    "def euclidian_dist(a, b, c, d):\n",
    "    dist = 0\n",
    "    dist = ((a - c)**2 + (b - d)**2)**(1/2)\n",
    "    return dist\n",
    "    \n",
    "c1 = x_test['principal component 1']\n",
    "c2 = x_test['principal component 2']\n",
    "c1_train = x_train['principal component 1']\n",
    "c2_train = x_train['principal component 2']\n",
    "l = []\n",
    "ll = []\n",
    "y_pred = []\n",
    "d = 0\n",
    "\n",
    "for i in range(len(c1)):\n",
    "    for j in range(len(c1_train)):\n",
    "        d = euclidian_dist(c1[i], c2[i], c1_train[j], c2_train[j])\n",
    "        l.append((d, c1_train[j], c2_train[j]))\n",
    "    for k in l:\n",
    "        ll.append(k[0])\n",
    "    m = min(ll)\n",
    "    for k in l:\n",
    "        if k[0] == m:\n",
    "            x_co = k[1]\n",
    "            y_co = k[2]\n",
    "            break\n",
    "            \n",
    "    for k in l1:\n",
    "        if k[1] == x_co and k[2] == y_co:\n",
    "            y_pred.append(k[0])\n",
    "            break\n",
    "            \n",
    "    l = []\n",
    "    ll = []\n",
    "    \n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = y_test.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy using KNN is  0.6\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "acc = accuracy_score(y_pred, a)\n",
    "\n",
    "print(\"Accuracy using KNN is \", acc)"
   ]
  }
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