K_means_with_Iris_Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import sklearn.metrics as sm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.1  3.5  1.4  0.2]\n",
      " [ 4.9  3.   1.4  0.2]\n",
      " [ 4.7  3.2  1.3  0.2]\n",
      " [ 4.6  3.1  1.5  0.2]\n",
      " [ 5.   3.6  1.4  0.2]\n",
      " [ 5.4  3.9  1.7  0.4]\n",
      " [ 4.6  3.4  1.4  0.3]\n",
      " [ 5.   3.4  1.5  0.2]\n",
      " [ 4.4  2.9  1.4  0.2]\n",
      " [ 4.9  3.1  1.5  0.1]\n",
      " [ 5.4  3.7  1.5  0.2]\n",
      " [ 4.8  3.4  1.6  0.2]\n",
      " [ 4.8  3.   1.4  0.1]\n",
      " [ 4.3  3.   1.1  0.1]\n",
      " [ 5.8  4.   1.2  0.2]\n",
      " [ 5.7  4.4  1.5  0.4]\n",
      " [ 5.4  3.9  1.3  0.4]\n",
      " [ 5.1  3.5  1.4  0.3]\n",
      " [ 5.7  3.8  1.7  0.3]\n",
      " [ 5.1  3.8  1.5  0.3]\n",
      " [ 5.4  3.4  1.7  0.2]\n",
      " [ 5.1  3.7  1.5  0.4]\n",
      " [ 4.6  3.6  1.   0.2]\n",
      " [ 5.1  3.3  1.7  0.5]\n",
      " [ 4.8  3.4  1.9  0.2]\n",
      " [ 5.   3.   1.6  0.2]\n",
      " [ 5.   3.4  1.6  0.4]\n",
      " [ 5.2  3.5  1.5  0.2]\n",
      " [ 5.2  3.4  1.4  0.2]\n",
      " [ 4.7  3.2  1.6  0.2]\n",
      " [ 4.8  3.1  1.6  0.2]\n",
      " [ 5.4  3.4  1.5  0.4]\n",
      " [ 5.2  4.1  1.5  0.1]\n",
      " [ 5.5  4.2  1.4  0.2]\n",
      " [ 4.9  3.1  1.5  0.1]\n",
      " [ 5.   3.2  1.2  0.2]\n",
      " [ 5.5  3.5  1.3  0.2]\n",
      " [ 4.9  3.1  1.5  0.1]\n",
      " [ 4.4  3.   1.3  0.2]\n",
      " [ 5.1  3.4  1.5  0.2]\n",
      " [ 5.   3.5  1.3  0.3]\n",
      " [ 4.5  2.3  1.3  0.3]\n",
      " [ 4.4  3.2  1.3  0.2]\n",
      " [ 5.   3.5  1.6  0.6]\n",
      " [ 5.1  3.8  1.9  0.4]\n",
      " [ 4.8  3.   1.4  0.3]\n",
      " [ 5.1  3.8  1.6  0.2]\n",
      " [ 4.6  3.2  1.4  0.2]\n",
      " [ 5.3  3.7  1.5  0.2]\n",
      " [ 5.   3.3  1.4  0.2]\n",
      " [ 7.   3.2  4.7  1.4]\n",
      " [ 6.4  3.2  4.5  1.5]\n",
      " [ 6.9  3.1  4.9  1.5]\n",
      " [ 5.5  2.3  4.   1.3]\n",
      " [ 6.5  2.8  4.6  1.5]\n",
      " [ 5.7  2.8  4.5  1.3]\n",
      " [ 6.3  3.3  4.7  1.6]\n",
      " [ 4.9  2.4  3.3  1. ]\n",
      " [ 6.6  2.9  4.6  1.3]\n",
      " [ 5.2  2.7  3.9  1.4]\n",
      " [ 5.   2.   3.5  1. ]\n",
      " [ 5.9  3.   4.2  1.5]\n",
      " [ 6.   2.2  4.   1. ]\n",
      " [ 6.1  2.9  4.7  1.4]\n",
      " [ 5.6  2.9  3.6  1.3]\n",
      " [ 6.7  3.1  4.4  1.4]\n",
      " [ 5.6  3.   4.5  1.5]\n",
      " [ 5.8  2.7  4.1  1. ]\n",
      " [ 6.2  2.2  4.5  1.5]\n",
      " [ 5.6  2.5  3.9  1.1]\n",
      " [ 5.9  3.2  4.8  1.8]\n",
      " [ 6.1  2.8  4.   1.3]\n",
      " [ 6.3  2.5  4.9  1.5]\n",
      " [ 6.1  2.8  4.7  1.2]\n",
      " [ 6.4  2.9  4.3  1.3]\n",
      " [ 6.6  3.   4.4  1.4]\n",
      " [ 6.8  2.8  4.8  1.4]\n",
      " [ 6.7  3.   5.   1.7]\n",
      " [ 6.   2.9  4.5  1.5]\n",
      " [ 5.7  2.6  3.5  1. ]\n",
      " [ 5.5  2.4  3.8  1.1]\n",
      " [ 5.5  2.4  3.7  1. ]\n",
      " [ 5.8  2.7  3.9  1.2]\n",
      " [ 6.   2.7  5.1  1.6]\n",
      " [ 5.4  3.   4.5  1.5]\n",
      " [ 6.   3.4  4.5  1.6]\n",
      " [ 6.7  3.1  4.7  1.5]\n",
      " [ 6.3  2.3  4.4  1.3]\n",
      " [ 5.6  3.   4.1  1.3]\n",
      " [ 5.5  2.5  4.   1.3]\n",
      " [ 5.5  2.6  4.4  1.2]\n",
      " [ 6.1  3.   4.6  1.4]\n",
      " [ 5.8  2.6  4.   1.2]\n",
      " [ 5.   2.3  3.3  1. ]\n",
      " [ 5.6  2.7  4.2  1.3]\n",
      " [ 5.7  3.   4.2  1.2]\n",
      " [ 5.7  2.9  4.2  1.3]\n",
      " [ 6.2  2.9  4.3  1.3]\n",
      " [ 5.1  2.5  3.   1.1]\n",
      " [ 5.7  2.8  4.1  1.3]\n",
      " [ 6.3  3.3  6.   2.5]\n",
      " [ 5.8  2.7  5.1  1.9]\n",
      " [ 7.1  3.   5.9  2.1]\n",
      " [ 6.3  2.9  5.6  1.8]\n",
      " [ 6.5  3.   5.8  2.2]\n",
      " [ 7.6  3.   6.6  2.1]\n",
      " [ 4.9  2.5  4.5  1.7]\n",
      " [ 7.3  2.9  6.3  1.8]\n",
      " [ 6.7  2.5  5.8  1.8]\n",
      " [ 7.2  3.6  6.1  2.5]\n",
      " [ 6.5  3.2  5.1  2. ]\n",
      " [ 6.4  2.7  5.3  1.9]\n",
      " [ 6.8  3.   5.5  2.1]\n",
      " [ 5.7  2.5  5.   2. ]\n",
      " [ 5.8  2.8  5.1  2.4]\n",
      " [ 6.4  3.2  5.3  2.3]\n",
      " [ 6.5  3.   5.5  1.8]\n",
      " [ 7.7  3.8  6.7  2.2]\n",
      " [ 7.7  2.6  6.9  2.3]\n",
      " [ 6.   2.2  5.   1.5]\n",
      " [ 6.9  3.2  5.7  2.3]\n",
      " [ 5.6  2.8  4.9  2. ]\n",
      " [ 7.7  2.8  6.7  2. ]\n",
      " [ 6.3  2.7  4.9  1.8]\n",
      " [ 6.7  3.3  5.7  2.1]\n",
      " [ 7.2  3.2  6.   1.8]\n",
      " [ 6.2  2.8  4.8  1.8]\n",
      " [ 6.1  3.   4.9  1.8]\n",
      " [ 6.4  2.8  5.6  2.1]\n",
      " [ 7.2  3.   5.8  1.6]\n",
      " [ 7.4  2.8  6.1  1.9]\n",
      " [ 7.9  3.8  6.4  2. ]\n",
      " [ 6.4  2.8  5.6  2.2]\n",
      " [ 6.3  2.8  5.1  1.5]\n",
      " [ 6.1  2.6  5.6  1.4]\n",
      " [ 7.7  3.   6.1  2.3]\n",
      " [ 6.3  3.4  5.6  2.4]\n",
      " [ 6.4  3.1  5.5  1.8]\n",
      " [ 6.   3.   4.8  1.8]\n",
      " [ 6.9  3.1  5.4  2.1]\n",
      " [ 6.7  3.1  5.6  2.4]\n",
      " [ 6.9  3.1  5.1  2.3]\n",
      " [ 5.8  2.7  5.1  1.9]\n",
      " [ 6.8  3.2  5.9  2.3]\n",
      " [ 6.7  3.3  5.7  2.5]\n",
      " [ 6.7  3.   5.2  2.3]\n",
      " [ 6.3  2.5  5.   1.9]\n",
      " [ 6.5  3.   5.2  2. ]\n",
      " [ 6.2  3.4  5.4  2.3]\n",
      " [ 5.9  3.   5.1  1.8]]\n"
     ]
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "print iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "print iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = pd.DataFrame(iris.data, columns=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'])\n",
    "y = pd.DataFrame(iris.target, columns=['Target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Sepal Length</th>\n",
       "      <th>Sepal Width</th>\n",
       "      <th>Petal Length</th>\n",
       "      <th>Petal Width</th>\n",
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
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       "      <td>1.4</td>\n",
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       "      <td>4.6</td>\n",
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      "text/plain": [
       "   Sepal Length  Sepal Width  Petal Length  Petal Width\n",
       "0           5.1          3.5           1.4          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2\n",
       "3           4.6          3.1           1.5          0.2\n",
       "4           5.0          3.6           1.4          0.2"
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     "execution_count": 6,
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    "x.head()"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Target</th>\n",
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      "text/plain": [
       "   Target\n",
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0"
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    "y.head()"
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   "execution_count": 8,
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YUmTuvlsVR3/i4uDSS9WMAjSyRKtWeaNguFwadq08vaDo0EHDyPkfjzEaW/mG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\nUIkxAABUNV7DTD9NR4h/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad92422550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3))\n",
    "colors = np.array(['red', 'green', 'blue'])\n",
    "iris_targets_legend = np.array(iris.target_names)\n",
    "red_patch = mpatches.Patch(color='red', label='Setosa')\n",
    "green_patch = mpatches.Patch(color='green', label='Versicolor')\n",
    "blue_patch = mpatches.Patch(color='blue', label='Virginica')\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(x['Sepal Length'], x['Sepal Width'], c=colors[y['Target']])\n",
    "plt.title('Sepal Length vs Sepal Width')\n",
    "plt.legend(handles=[red_patch, green_patch, blue_patch])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(x['Petal Length'], x['Petal Width'], c= colors[y['Target']])\n",
    "plt.title('Petal Length vs Petal Width')\n",
    "plt.legend(handles=[red_patch, green_patch, blue_patch])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_k_mean_model = KMeans(n_clusters=3)\n",
    "iris_k_mean_model.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 2 2 0 2 2 2 2\n",
      " 2 2 0 0 2 2 2 2 0 2 0 2 0 2 2 0 0 2 2 2 2 2 0 2 2 2 2 0 2 2 2 0 2 2 2 0 2\n",
      " 2 0]\n"
     ]
    }
   ],
   "source": [
    "print iris_k_mean_model.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.9016129   2.7483871   4.39354839  1.43387097]\n",
      " [ 5.006       3.418       1.464       0.244     ]\n",
      " [ 6.85        3.07368421  5.74210526  2.07105263]]\n"
     ]
    }
   ],
   "source": [
    "print iris_k_mean_model.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fad8f869c50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8e/bUO6NmBvklgUmlyEF0dDQ9e/bklltuYcSIEQD069eP8ePHc+ONNxIdHc2B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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad8fc10c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3))\n",
    "\n",
    "colors = np.array(['red', 'green', 'blue'])\n",
    "\n",
    "predictedY = np.choose(iris_k_mean_model.labels_, [1, 0, 2]).astype(np.int64)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(x['Petal Length'], x['Petal Width'], c=colors[y['Target']])\n",
    "plt.title('Before classification')\n",
    "plt.legend(handles=[red_patch, green_patch, blue_patch])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(x['Petal Length'], x['Petal Width'], c=colors[predictedY])\n",
    "plt.title(\"Model's classification\")\n",
    "plt.legend(handles=[red_patch, green_patch, blue_patch])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89333333333333331"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.accuracy_score(predictedY, y['Target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretation of Confusion Matrix\n",
    "Correctly identifed all 0 classes as 0’s\n",
    "correctly classified 48 class 1’s but miss-classified 2 class 1’s as class 2\n",
    "correctly classified 36 class 2’s but miss-classified 14 class 2’s as class 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50,  0,  0],\n",
       "       [ 0, 48, 14],\n",
       "       [ 0,  2, 36]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.confusion_matrix(predictedY, y['Target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}