kNN_classification_iris_solution.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the iris data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "--2021-03-05 09:05:24--  https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4551 (4,4K) [application/x-httpd-php]\n",
      "Saving to: ‘iris.data.6’\n",
      "\n",
      "iris.data.6         100%[===================>]   4,44K  --.-KB/s    in 0,001s  \n",
      "\n",
      "2021-03-05 09:05:25 (6,04 MB/s) - ‘iris.data.6’ saved [4551/4551]\n",
      "\n",
      "[5.  3.4 1.5 0.2 1. ]\n"
     ]
    }
   ],
   "source": [
    "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\n",
    "\n",
    "irisTypes=dict()\n",
    "irisTypes[b'Iris-setosa']=1\n",
    "irisTypes[b'Iris-versicolor']=2\n",
    "irisTypes[b'Iris-virginica']=3\n",
    "\n",
    "def conv(inputString):\n",
    "    return irisTypes[inputString]\n",
    "\n",
    "data = np.loadtxt('iris.data', delimiter=',', converters={4: conv})\n",
    "print(data[7,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[:,:4]\n",
    "Y=data[:,4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the kNN classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=6)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "neigh = KNeighborsClassifier(n_neighbors=k)\n",
    "neigh.fit(X, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do some prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(neigh.predict(X)-Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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