ML Assignment 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7f9de4c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4d0833a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"knn_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "06f78e9e",
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Colour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>Orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>Orange</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X  Y  Colour\n",
       "0  2  4  Orange\n",
       "1  4  4    Blue\n",
       "2  4  6  Orange\n",
       "3  4  2  Orange\n",
       "4  6  2    Blue\n",
       "5  6  4  Orange"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9c2db9cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,:-1].values\n",
    "Y = data.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c982e784",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Orange', 'Blue', 'Orange', 'Orange', 'Blue', 'Orange'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "96a3eea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = ['b' if x==\"Blue\" else 'orange' for x in Y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "28801e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['orange', 'b', 'orange', 'orange', 'b', 'orange']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "368e6588",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b7681aa3a0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ItIVeVQ8BmWbMjcCNwwolSZo5vykqSS1hoUtSS1joktQSFroktYSFLkktYaFLUktY6JLUEha6JLWEhS5JLWGhS1JLWOiS1BIWuiS1hIUuSS1hoUtSS1joktQSTW5wIb0z7NsDP/uf8MIP4Lgz4P3XwDHeSVHDsX8//M3fwL33wtgYXHMNvO99w93HtIWe5DRgA3AycABYX1Vf7hkT4MvAZcCrwDVVtXm4UaVZtPcF+NtV8NoO2LcbFhwLT/4xfOxBOP6sUafT29wbb8AnPgGPPgq7dsHRR8Of/znceiv81m8Nbz9NTrnsA/6gqv4VcD5wfZIP9oy5FDij+1gL3DS8iNIcePy/wp7tnTIH2L8H3tgJP/i3o82lVrjlFnjkkU6ZA7z+OuzZA1dfDa+9Nrz9TFvoVfX8m0fbVfUKsBU4tWfYFcCG6tgILE2yfHgxpVn27LfhwBsHr9/5Y3j9F3OfR61yyy2we/fB6xP4wQ+Gt58ZfSiaZCVwDvBwz6ZTgWenPN/OwaVPkrVJJpJMTE5OzjCqNIuOOnrwtvhRk47Mscf2X18FxxwzvP00LvQkxwHfBj5bVTt7N/f5lTpoRdX6qhqvqvGxsbGZJZVm07/8d53z5lNlAYxdBIuWjCaTWmPtWli8+OD1ixfDqlXD20+jQk+yiE6Zf6OqvtNnyHbgtCnPVwDPHXk8aY6c+XlY9uuwYHGn2BcugcWnwwUbRp1MLfCbvwlr1nSOxt/9bliyBJYuhbvugqOGePF4qg46kH7rgM4VLF8H/rmqPjtgzGrg9+hc5XIecENVHfLvzvj4eE1MTBxOZmn2vPgo/OKHsHglnPRROGrBqBOpRbZtg/vvhxNOgNWrD+90S5JNVTXeb1uTk4MXAr8LPJFkS3fdHwG/BFBV64C76ZT5NjqXLa6ZeUxpHjjx1zoPaRZ84AOdx2yZttCr6iH6nyOfOqaA64cVSpI0c371X5JawkKXpJaw0CWpJSx0SWqJaS9bnLUdJ5PATw/z15cBLwwxzrDM11wwf7OZa2bMNTNtzHV6VfX9ZubICv1IJJkYdB3mKM3XXDB/s5lrZsw1M++0XJ5ykaSWsNAlqSXeroW+ftQBBpivuWD+ZjPXzJhrZt5Rud6W59AlSQd7ux6hS5J6WOiS1BLzttCTnJbk/iRbkzyZ5DN9xiTJDUm2JXk8ybnzJNfFSV5OsqX7+MIc5DomySNJHuvm+mKfMaOYrya55ny+pux7QZIfJrmrz7Y5n6+GuUY5X88keaK734P+/etRzVmDXCOZsyRLk3wryVPdzrigZ/tw56uq5uUDWA6c211eAvw/4IM9Yy4D7qHzr0GeDzw8T3JdDNw1x/MV4Lju8iI6twk8fx7MV5Nccz5fU/b9n4Fb++1/FPPVMNco5+sZYNkhto9kzhrkGsmc0bmXxL/vLh8NLJ3N+Zq3R+g1T29O3TDXnOvOQfee4izqPno/8R7FfDXJNRJJVgCrgZsHDBnJzc8b5JrPvGF8V5L3ABcBXwWoqter6qWeYUOdr3lb6FPlCG9OPVsOkQvggu5phnuSnDlHeRakcxOSHcB9VTUv5qtBLhjBfAFfAj4HHBiwfVTvry9x6FwwmvmCzh/j7ybZlGRtn+2jmrPpcsHcz9n7gUnga93TZzcn6b2z6FDna94XeoZwc+rZME2uzXT+vYWzgK8Ad8xFpqraX1Vn07mn66okH+oZMpL5apBrzucryeXAjqradKhhfdbN6nw1zDWS91fXhVV1LnApcH2Si3q2j+q/yelyjWLOFgLnAjdV1TnAbuAPe8YMdb7mdaFnnt6cerpcVbXzzdMMVXU3sCjJstnONWX/LwEPAJ/s2TTSm3kPyjWi+boQ+FSSZ4BvApckuaVnzCjma9pco3x/VdVz3Z87gNuB3nsHj+Q9Nl2uEc3ZdmD7lP8j/Radgu8dM7T5mreFniR0zj1traq/GDDsTuDq7ifF5wMvV9Xzo86V5OTuOJKsojPPL85yrrEkS7vLxwIfA57qGTaK+Zo21yjmq6o+X1UrqmolcBXw91X16Z5hcz5fTXKNYr66+1qcZMmby8DHgR/1DBvFe2zaXCN6j/0ceDbJr3RXfRT4cc+woc5Xk5tEj8p8vTl1k1xXAtcl2QfsAa6q7kfas2g58PUkC+i8WW+rqruSXDsl1yjmq0muUcxXX/NgvprkGtV8nQTc3u3FhcCtVXXvPJizJrlGNWe/D3wjydHA08Ca2Zwvv/ovSS0xb0+5SJJmxkKXpJaw0CWpJSx0SWoJC12SWsJCl6SWsNAlqSX+PwXo4NabAZgaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,:-1],X[:,-1],color=c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "692bbef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier,NearestNeighbors\n",
    "knn = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3d3fd02e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f36fcfbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = knn.predict([[6,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c6fdc38b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Orange']\n"
     ]
    }
   ],
   "source": [
    "print(prediction)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ee5897e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_2 = KNeighborsClassifier(weights='distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1f8b161a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(weights='distance')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_2.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "83b3f16b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Orange']\n"
     ]
    }
   ],
   "source": [
    "prediction2 = knn_2.predict([[6,6]])\n",
    "print(prediction2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2d5f10f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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