{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose you have a list of positions of possible clients of Uber in Lisbon (Passageiros.csv). \n", "How many cars could you use and where they could be positioned in order to reduce time?\n", "\n", "Use a cluster analysis appraoch to support the solution of this problem.\n", "* import the libraries needed\n", "* import dataset from Passageiros.csv file\n", "* Verify imported data \n", "* verify data types and convert into numeric if needed. Use for example, df['x']=pd.to_numeric(df['x'], errors='coerce')\n", "* plot a scatter chart\n", "* create a X dataframe including only numeric columns\n", "* calculete WCSS using X dataframe:\n", "\n", " wcss = []\n", "\n", " for i in range(1, 11):\n", "\n", " kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n", " \n", " kmeans.fit(df)\n", " \n", " wcss.append(kmeans.inertia_)\n", "\n", " plt.plot(range(1, 11), wcss)\n", "\n", " plt.title('Elbow Method')\n", "\n", " plt.xlabel('Number of clusters')\n", "\n", " plt.ylabel('WCSS')\n", "\n", " plt.show()\n", "\n", "\n", "* plot a scatter chart showing centroids of the clusters estimated\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "from sklearn.cluster import KMeans" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#import dataset from Passageiros.csv file\n", "df=pd.read_csv('Passageiros.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
XYName
0-9.16387438.711563Passageiro 1
1-9.19944738.703342Passageiro 2
2-9.14375238.729060Passageiro 3
3-9.15041038.755656Passageiro 4
4-9.13633438.758534Passageiro 5
\n", "
" ], "text/plain": [ " X Y Name\n", "0 -9.163874 38.711563 Passageiro 1\n", "1 -9.199447 38.703342 Passageiro 2\n", "2 -9.143752 38.729060 Passageiro 3\n", "3 -9.150410 38.755656 Passageiro 4\n", "4 -9.136334 38.758534 Passageiro 5" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Verify imported data \n", "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "X float64\n", "Y float64\n", "Name object\n", "dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#verify data types and convert into numeric if needed\n", "# e.g. df['x']=pd.to_numeric(df['x'], errors='coerce')\n", "df.dtypes" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAD8CAYAAACPWyg8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAG+9JREFUeJzt3X2QXfV93/H3R8tiFrtITlkaIcEILEstg6IVbJBiGuIIiECZgkKHgkqoXQq09riNlVZjadCQoWGCHLm09YQhwyQmtDzbYNnGgOoYSAbGCK+QQCJCBAgPktyyjlGpIgXk1bd/3LOwWt279/z2Pp2z9/OaubP3/s7D/Z57997v/T2c31FEYGZmlmJapwMwM7PycfIwM7NkTh5mZpbMycPMzJI5eZiZWTInDzMzS+bkYWZmyZw8zMwsmZOHmZklO6bTAaQ48cQTY86cOZ0Ow8ysVLZs2fLTiOhv5j5LlTzmzJnD0NBQp8MwMysVSW82e59utjIzs2ROHmZmlszJw8zMkjl5mJlZMicPMzNL5uRhZmbJnDzMzCyZk4eZmSVz8jAzs2ROHmZmlszJw8zMkjl5mJlZMicPMzNLVjd5SDpO0nOSXpD0kqSbsvLzJT0vaZukpyXNrbJtr6S7JG2XtFPS2jHLLpK0S9KrktY097DMzKyV8tQ83geWRsRCYAC4SNIS4HbgqogYAO4F1lXZ9nLgYxGxADgb+LeS5kjqAW4DLgbOAFZKOqPxwzEzs3aomzyiYn/2sDe7RXY7ISufDuyttjnwcUnHAH3AB8B7wDnAqxHxekR8ANwPXNrIgZiZWfvkuhhUVlPYAswFbouIzZKuBR6VdJBKQlhSZdNvUUkKPwGOB1ZFxM8kzQLeHrPebmDx5A/DzMzaKVeHeUSMZM1Ts4FzJJ0JrAKWR8Rs4E7g1iqbngOMACcDpwH/UdLpgKo9TbXnlnS9pCFJQ8PDw3nCNTOzFksabRUR+4CnqPRVLIyIzdmiB4DPVNnkXwKPR8ShiHgHeAYYpFLTOGXMerOp3uxFRNwREYMRMdjf39RL8JqZ2STlGW3VL2lGdr8PuADYCUyXNC9b7cKsbLy3gKWq+DiVpq2XgR8Dn5Z0mqRjgSuB7zZ8NGZm1hZ5+jxmAndl/R7TgAcj4hFJ1wEPSToMvAtcAyDpEmAwIm6kMqLqTmAHlaaqOyPixWy9LwGbgB7gGxHxUnMPzczMWkURVbsaCmlwcDCGhoY6HYaZWalI2hIRg83cp88wNzOzZE4eZmaWzMnDzMySOXmYmVkyJw8zM0vm5GFmZsmcPMzMLFmuiRHNzLrdxq172LBpF3v3HeTkGX2sXjafFYtmdTqsjnHyMDOrY+PWPax9eDsHD40AsGffQdY+vB2gaxOIm63MzOrYsGnXh4lj1MFDI2zYtKtDEXWek4eZWR179x1MKu8GTh5mZnWcPKMvqbwbOHmYmdWxetl8+np7jijr6+1h9bL5HYqo89xhbmZWx2inuEdbfcTJw8wshxWLZnV1shjPzVZmZpbMycPMzJI5eZiZWTInDzMzS+bkYWZmyZw8zMwsmZOHmZklc/IwM7NkTh5mZpbMZ5ibmTVBt10sysnDzKxB3XixqLrNVpKOk/ScpBckvSTppqz8fEnPS9om6WlJc6tse1W2fPR2WNJAtmylpO2SXpT0uKQTm394Zs23cesezl3/BKet+T7nrn+CjVv3dDok67BuvFhUnj6P94GlEbEQGAAukrQEuB24KiIGgHuBdeM3jIh7ImIgW+dq4I2I2CbpGOC/A78eEb8EvAh8qTmHZNY6o78w9+w7SPDRL0wnkO7WjReLqps8omJ/9rA3u0V2OyErnw7srbOrlcB92X1lt49LUrafetubdVw3/sK0+rrxYlG5RltJ6pG0DXgH+EFEbAauBR6VtJtKrWJ9nd1cQZY8IuIQ8AVgO5WkcQbwpzWe+3pJQ5KGhoeH84Rr1jLd+AvT6uvGi0XlSh4RMZI1Pc0GzpF0JrAKWB4Rs4E7gVtrbS9pMXAgInZkj3upJI9FwMlUmq3W1njuOyJiMCIG+/v78x+ZWQt04y9Mq2/FolncctkCZs3oQ8CsGX3cctmCKdtZDomjrSJin6SngIuBhVkNBOAB4PEJNr2Sj5qsoNJ3QkS8BiDpQWBNSixmnbB62fwjRtXA1P+Fafl028Wi8oy26pc0I7vfB1wA7ASmS5qXrXZhVlZt+2nA5cD9Y4r3AGdIGq1K1NzerEi68RemWTV5ah4zgbsk9VBJNg9GxCOSrgMeknQYeBe4BkDSJcBgRNyYbX8esDsiXh/dYUTszYb8/qWkQ8CbwOebdVBmrdRtvzDNqlFEdDqG3AYHB2NoaKjTYZiZlYqkLREx2Mx9em4rMzNL5uRhZmbJnDzMzCyZk4eZmSVz8jAzs2ROHmZmlszJw8zMkjl5mJlZMicPMzNL5uRhZmbJnDzMzCyZk4eZmSVLup6HWTdZt3E7921+m5EIeiRWLj6Fm1cs6HRYZoXg5GFWxbqN27n72bc+fDwS8eFjJxAzN1uZVXXf5reTys26jZOHWRUjNa5zU6vcrNs4eZhV0SMllZt1GycPsyqWnP7JpHKzbuPkYVbFG397MKncrNs4eZhVsXdf9SRRq9ys2zh5mFVx8oy+pHKzbuPzPGxK2bh1Dxs27WLvvoOcPKOP1cvms2LRrOT9rF42n7UPb+fgoZEPy/p6e1i9bH4zw7VxmvX+Wes5ediUsXHrniO+8PfsO8jah7cDJH8Bja7vL7L2aeb7Z63n5GFTxoZNu46oKQAcPDTChk27JvXls2LRLH9ptVGz3z9rLfd52JThTu5y8/tXLnWTh6TjJD0n6QVJL0m6KSs/X9LzkrZJelrS3CrbXpUtH70dljSQLTtW0h2SXpH0sqR/3vzDs27iTu5y8/tXLnlqHu8DSyNiITAAXCRpCXA7cFVEDAD3AuvGbxgR90TEQLbO1cAbEbEtW3wD8E5EzAPOAP6i8cOxbrZ62Xz6enuOKHMnd3n4/SuXun0eERHA/uxhb3aL7HZCVj4d2FtnVyuB+8Y8vgb4x9lzHAZ+mjtqsyrcyV1ufv/KRZFjojdJPcAWYC5wW0R8RdKvAhuBg8B7wJKIeG+CfbwGXBoROyTNALYD3wQ+C7wGfCki/k+V7a4Hrgc49dRTz37zzTfTjtBsivPwVqtH0paIGGzmPnN1mEfESNb0NBs4R9KZwCpgeUTMBu4Ebq21vaTFwIGI2JEVHZPt65mIOAv4EfC1Gs99R0QMRsRgf39/3uMy6wqjw1v37DtI8NHw1o1b93Q6NJvikkZbRcQ+4CngYmBhRGzOFj0AfGaCTa/kyCarvwUOAN/OHn8TOCslFjObeHirWSvlGW3VnzUzIakPuADYCUyXNC9b7cKsrNr204DLgftHy7J+lO9RabICOB/4q8kdgln38vBW65Q8JwnOBO7K+j2mAQ9GxCOSrgMeknQYeJdKBziSLgEGI+LGbPvzgN0R8fq4/X4F+J+S/hswDPzrxg+nuNwuba1w8ow+9lRJFB7eaq2Wq8O8KAYHB2NoaKjTYSQbP+0CVIYg3nLZAieQEivCDwL/b1keHeswt8a4XXrqKUpH9YpFs7jlsgXMmtGHgFkz+pw4rC08t1UbuF166inSPEyeg8s6wTWPNqjV/jxN8pDKkvIPAut2Th6TsHHrHs5d/wSnrfk+565/om4CqDbtAsBIhMfkl5TnYbJu5+SRaDJt3aPt0j3SUcvc91FOzZyHKfXHiFkROHkkmmzn94pFszhcY2SbmzrKp1kd1UXpeDdL5Q7zRI20dXtM/tTSjI7qInW8m6VwzSNRI23dnnLaxnPHu5WVk0eiRhKAx+TbeO54t7Jys1WiRq854DH5NtbqZfOrniHu2qgVnZPHJBQlARRhegxrzOj7ddP3XuLdA4cA+NgxbhCw4nPyKKnxcxqNjtIBnEBK6O8PHf7w/r6Dh1j78HaG3vwZT7487B8HVkj+iVNSni9r6qj1Xt797FsewmuF5eRRUh6lM3Xkfc/848CKxMmjpDxKZ+pIec/848CKwsmjpHzOyNRRa+6zapr148BTolij3GFeUo0OGbbmaMaIt/Hv5TSJkRpT2Rz44Ods3LqnoffZgy2sGXwlQSu8og5JbtVV/Krtd6xqz5HyGp27/omq0+TMmtHHM2uWTjpuKy5fSdC6TpEnDmzViLexMxFUM/45Ul8jD7awZnDysEJr15DkyfQBtPJLeMWiWTyzZilHT+J/9HOkvkYebGHN4ORhhdaOX8mTrd2040s4z3OkvkYebGHN4ORhhdaOL+jJ1m5WL5tPb8+RdYPeHjX1SzjPF33qa+QJOq0ZPNrKWqrRzu52TBzYUO1m/HiTJo8/yTOqbjKvUVHmZ7PycvKwlmnGkNB2DEme7EW6NmzaxaHDR2aLQ4ej6RdyqvdFP9nXqKij2KwcnDysZZp1lbxW/0qebO2mXo0l75dzs84VSdnG53pYo+r2eUg6TtJzkl6Q9JKkm7Ly8yU9L2mbpKclza2y7VXZ8tHbYUkD49b5rqQdzTskK4qyDAmdbB/ARH0NeTvhOzUU2RNrWqPy1DzeB5ZGxH5JvcDTkh4DbgcujYidkr4IrAM+P3bDiLgHuAdA0gLgOxGxbXS5pMuA/U05EiucMl2zfTK1m4lqLPVqXaO1jWqvTzuuYV6WxG7FVbfmERWjX/C92S2y2wlZ+XRgb51drQTuG30g6RPA7wI3J8ZsJVGWIaF5z/EYvx5Qs8Yy0Zfz2NpGLa3+Eq+VwKf39bb0eW3qyNXnIakH2ALMBW6LiM2SrgUelXQQeA9YUmc3VwCXjnn8+8B/AQ4kR22lUIb5t/K2/dda75bLFlSd0mOiWle1Wkm19Vpp9bL5rP7mC0d1+P9dE+bOsu6Q6zyPiBiJiAFgNnCOpDOBVcDyiJgN3AncWmt7SYuBAxGxI3s8AMyNiG/Xe25J10sakjQ0PDycJ1wrkNEzpf9m/W/yzJqlhftSytv2n9pHMFGtq16toh21sxWLZvGJ447+7XhoJNzvYbkkjbaKiH2SngIuBhZGxOZs0QPA4xNseiVjmqyAXwHOlvRGFsNJkp6KiM9Wec47gDugMjFiSrxWXu0aRpq37b9eM1StWKuV1+rrgErTV7tqZ/uya6aP534Py6Nu8pDUDxzKEkcfcAHwVWC6pHkR8QpwIbCzxvbTgMuB80bLIuJ2Kh3uSJoDPFItcVh3msww0skmm7yd+rXWm97XO2Gs1WKo1dHe7rO8yzSgwYonT7PVTOBJSS8CPwZ+EBGPANcBD0l6AbgaWA0g6RJJ/3nM9ucBuyPi9eaGblNVahNRI8Nd83bq11pPInnIa1GmBynLgAYrJl/PwwrntDXfrzrLh4C/Wf+bR5U3en2KRk7mW/XAtqRYi8ZnmXeHVlzPw2eYW+GkNqc0es5C3nM8qq1Xq/+iU00/qcnAc1zZZHlWXSuc1OaU1Fllm3n97iI1/RT5wlk29Th5WOGk9gmkfIFP5gt2omRTlP4L8JQj1l5utrJCSmlOSTkZMXWyxjwjv5rZ9DO22Wl6Xy9SZUhtniYoTzli7eTkYVNC3i/w1C/YySSbyXZAj09U+w5+dB5GnuHKEw0nNms2N1tZV0ntH0lJNo32OdSbtqReE9TqZfPpnXb0Vc9HpxwxayYnD+sqreyMb7TPIU/z0kTreMoRaycnD+sqreyMb7TPIc/w3nrreMoRaxf3eVjXaVVnfK0+hxnH93Lu+ifqbl9t2pKx8gwB9pQj1i5OHmZ15Ek2G7fu4cAHPz+qvLdH7P/7n/NuViOYqON7fKJKHW0Fk7+krlkqJw+zBo0fJTVqRjbKaeyoKTiyH6RajaaRYb9luIaKTQ2e28qsQRPNrbU3G3lVTV9vT8dn1rXu0Iq5rdxhbtagiTrKa/U19Eg+G9xKzcnDrEETDeetNVprpEaN36OirCycPMwaNNFw3lpDg2clnqxoVjTuMDdrUL1O6lqd4B4VZWXm5GHWBKmjpDwqysrOycOsQ3whJiszJw+zFvDlXW2qc/Iwm4SJkkOea4CYlZ1HW5klqjf1uq/oZ93AycMsUb3k4Cv6WTdw8jBLVC85pF5wyqyM3OdhlqjetOcTzWy7buN27tv8NiMR9EisXHwKN69Y0LbYzZrFNQ+zRPUuEFXrrPKhN3/G3c++9eHUJCMR3P3sW6zbuL3dh2DWsLqz6ko6DvhL4GNUairfiojfk3Q+sIFKAtoPfD4iXh237VXA6jFFvwScBbwCfBP4FDACfC8i1tQL1rPqWlFMZijup9Y+WnVOqx6J125Z3qpQzVoyq26eZqv3gaURsV9SL/C0pMeA24FLI2KnpC8C64DPj90wIu4B7smCXwB8JyK2SToe+FpEPCnpWOCHki6OiMead2hmrTOZE/xqTYZYq9ysyOo2W0XF/uxhb3aL7HZCVj4d2FtnVyuB+7J9HoiIJ7P7HwDPA7OTozcrkR4pqdysyHL1eUjqkbQNeAf4QURsBq4FHpW0G7gaWF9nN1eQJY9x+54B/DPghymBm5XNysWnJJWbFVmu5BERIxExQKV2cI6kM4FVwPKImA3cCdxaa3tJi4EDEbFjXPkxVBLK1yPi9RrbXi9pSNLQ8PBwroMyK6KbVyzgt5ec+mFNo0fit5ec6tFWVkrJl6GV9HvAAeDfRcSnsrJTgccj4owa2/xXYDgi/mBc+TeA/RHxH/I8tzvMzczSdeQytJL6s6YlJPUBFwA7gemS5mWrXZiVVdt+GnA5cP+48pup9JV8edLRm5lZR+QZbTUTuEtSD5Vk82BEPCLpOuAhSYeBd4FrACRdAgxGxI3Z9ucBu8c2S0maDdwAvAw8r0o1/o8i4k+adFxmZtZCyc1WneRmq6nHU5ebtV6nzvMwawlPXW5WXp6exDrGU5eblZdrHtYx3Tp1uZvqbCpwzcM6phunLq93ISmzsnDysI6pNzvtVOSmOpsq3GxlHTPaVNNNTTjd2lRnU4+Th3XUZGanHVXGvoN6F5IyKws3W1kplbXvoBub6mxqcvKwUipr30GtqwwWvcZkNp6brayUytx30EhTnVlRuOZhpdSNw3zNisTJw0rJfQdmneVmKyulbhzma1YkTh5WWu47MOscN1uZmVky1zys65Tx5EKzonHysK7ia4iYNYebrayrlPXkQrOicfKwrlLmkwvNisTJw7qKTy40aw4njzbauHUP565/gtPWfJ9z1z9R+En8piKfXGjWHO4wbxN31BaDTy40aw4njzaZqKPWX1zt5ZMLzRrnZqs2cUetmU0lTh5t4o5aM5tK6iYPScdJek7SC5JeknRTVn6+pOclbZP0tKS5Vba9Kls+ejssaSBbdrak7ZJelfR1SWr+4RWHO2rNbCrJU/N4H1gaEQuBAeAiSUuA24GrImIAuBdYN37DiLgnIgayda4G3oiIbdni24HrgU9nt4saPpoC8xXkzGwqqdthHhEB7M8e9ma3yG4nZOXTgb11drUSuA9A0kzghIj4Ufb4fwArgMcS4y8Vd9Sa2VSRa7SVpB5gCzAXuC0iNku6FnhU0kHgPWBJnd1cAVya3Z8F7B6zbHdWZmZmJZCrwzwiRrKmp9nAOZLOBFYByyNiNnAncGut7SUtBg5ExI7RompPU2Pb6yUNSRoaHh7OE66ZmbVY0miriNgHPAVcDCyMiM3ZogeAz0yw6ZVkTVaZ3VQS0ajZ1Gj2iog7ImIwIgb7+/tTwjUzsxbJM9qqX9KM7H4fcAGwE5guaV622oVZWbXtpwGXA/ePlkXET4D/J2lJNsrqXwHfaeRAzMysffL0ecwE7sr6PaYBD0bEI5KuAx6SdBh4F7gGQNIlwGBE3Jhtfx6wOyJeH7ffLwB/BvRR6Sif0p3lZmZTiSqDqcphcHAwhoaGOh2GmVmpSNoSEYPN3KfPMDczs2ROHmZmlszJw8zMkjl5mJlZMicPMzNL5uRhZmbJnDzMzCyZk4eZmSVz8jAzs2ROHmZmlszJw8zMkjl5mJlZMicPMzNL5uRhZmbJnDzMzCyZk4eZmSVz8jAzs2ROHmZmlszJw8zMkjl5mJlZMicPMzNL5uRhZmbJnDzMzCyZIqLTMeQmaRj4O+CnnY5lkk6kvLFDueMvc+zg+DupzLFDJf6PR0R/M3daquQBIGkoIgY7HcdklDl2KHf8ZY4dHH8nlTl2aF38brYyM7NkTh5mZpasjMnjjk4H0IAyxw7ljr/MsYPj76Qyxw4tir90fR5mZtZ5Zax5mJlZhxUueUhaKOlHkrZL+p6kE6qsc4qkJyXtlPSSpN8Zs2yDpJclvSjp25JmlCz+X5D0A0l/nf39ZNHiz9b7hqR3JO0YVz4g6VlJ2yQNSTqnPZE3Hnu27N9L2pW9L3/Y+qiPeO6G48+W/ydJIenE1kZ8xHM2+n9T+M9ttl6t+Mvyub0o+/9+VdKaMeXnS3o++9w+LWlu3SeNiELdgB8Dv5bdvwb4/SrrzATOyu7/A+AV4Izs8W8Ax2T3vwp8tWTx/yGwJru/pojxZ8vOA84Cdowr/1/Axdn95cBTJYr914E/Bz6WPT6pTK99tuwUYBPwJnBiWWIvw+e2TvyF/9wCPcBrwOnAscALY753XgH+SXb/i8Cf1X3Odh5gzhfhPT7qizkF+Ksc23wHuLBK+W8B95QpfmAXMDO7PxPYVdT4gTlVPkSbgCuy+yuBe0sU+4PABe18vZsZf1b+LWAh8Eabk0fDsY9ZXujPbY3/ncJ/boFfATaNebwWWDsm/sVjyv+g3nMWrtkK2AFckt2/nMoLUZOkOcAiYHOVxdcAjzUxtjwajf8fRcRPALK/J7UkytqS4q/iy8AGSW8DX6Pyj9gujcY+D/hVSZsl/YWkX25qdPU1FL+kS4A9EfFCswPLodHXfqzCf26rKMPndhbw9pjHu7MygGuBRyXtBq4G1td7wmMmHWoDJP058ItVFt1A5R/n65JuBL4LfDDBfj4BPAR8OSLeG7fsBuDnwD3NinvMvlsefys1K/4avgCsioiHJP0L4E+BCxqJd6wWx34M8ElgCfDLwIOSTo/s51gztCp+Scdn+/iNZsRZ4zla+dqPPkfhP7ed0oT4VaVs9H97FbA8IjZLWg3cSiWh1NbOqtUkqmLzgOdqLOul0kTyu1WWfQ74EXB82eKnw9XfvPFny+dwdPX9//JR9VnAeyWK/XHgs2Mevwb0lyF+YAHwDpXmqjeofAG/Bfxi0WMfU174z+0E/zuF/9xSo9kK6AdeG1N+Kjma2wvXbCXppOzvNGAd8MdV1hGVX7Q7I+LWccsuAr4CXBIRB1of8VGxNRQ/lV8Nn8vuf45Kf0jb5Im/jr3Ar2X3lwJ/3bzoJtaE2DdSiRlJ86h0KrZtQrxG4o+I7RFxUkTMiYg5VJokzoqI/92SYMdp9LUvw+e2jjJ8bn8MfFrSaZKOBa6kEve7wPTsfx7gQmBn3SftZIavkTV/h0rP/ytU2t1Gf8WeDDya3f+nVKpbLwLbstvybNmrVNr1Rsv/uGTx/0Pgh1S+dH8I/ELR4s8e3wf8BDhE5Yvq34w5ti1URnJsBs4uUezHAndTaT9+Hlhaptd+3L7eoL0d5o2+9oX/3NaJvyyf2+XZOq8BN4wp/y1ge/a5fQo4vd5z+gxzMzNLVrhmKzMzKz4nDzMzS+bkYWZmyZw8zMwsmZOHmZklc/IwM7NkTh5mZpbMycPMzJL9f/3/HvYDRxtkAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#plot a scatter chart\n", "plt.scatter(df[\"X\"], df[\"Y\"])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# create a X dataframe including only numeric columns\n", "X=df[[\"X\",\"Y\"]]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# calculete WCSS using X dataframe\n", "'''\n", "wcss = []\n", "for i in range(1, 11):\n", " kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n", " kmeans.fit(df)\n", " wcss.append(kmeans.inertia_)\n", "plt.plot(range(1, 11), wcss)\n", "plt.title('Elbow Method')\n", "plt.xlabel('Number of clusters')\n", "plt.ylabel('WCSS')\n", "plt.show()\n", "'''\n", "\n", "wcss = []\n", "for i in range(1, 11):\n", " kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n", " kmeans.fit(X)\n", " wcss.append(kmeans.inertia_)\n", "plt.plot(range(1, 11), wcss)\n", "plt.title('Elbow Method')\n", "plt.xlabel('Number of clusters')\n", "plt.ylabel('WCSS')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#plot a scatter chart showing centroids of the clusters estimated\n", "\n", "plt.scatter(df[\"X\"], df[\"Y\"])\n", "\n", "kmeans = KMeans(n_clusters=6, init='k-means++', max_iter=300, n_init=10, random_state=0)\n", "pred_y = kmeans.fit_predict(X)\n", "\n", "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }