How to Choose the Number of Clusters .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to Choose the Number of Clusters "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the same code as in the previous exercise, find the WCSS for clustering solutions with 1 to 10 clusters (you can try with more if you wish).\n",
    "\n",
    "Find the most suitable solutions, run them and compare the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data from the csv file: <i> 'Countries_exercise.csv'</i>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>name</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>-69.982677</td>\n",
       "      <td>12.520880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>66.004734</td>\n",
       "      <td>33.835231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Angola</td>\n",
       "      <td>17.537368</td>\n",
       "      <td>-12.293361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Anguilla</td>\n",
       "      <td>-63.064989</td>\n",
       "      <td>18.223959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Albania</td>\n",
       "      <td>20.049834</td>\n",
       "      <td>41.142450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>236</td>\n",
       "      <td>Samoa</td>\n",
       "      <td>-172.164851</td>\n",
       "      <td>-13.753243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>237</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>47.586762</td>\n",
       "      <td>15.909280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>238</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>25.083901</td>\n",
       "      <td>-29.000341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>27.774759</td>\n",
       "      <td>-13.458242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>240</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>29.851441</td>\n",
       "      <td>-19.004204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name   Longitude   Latitude\n",
       "0           Aruba  -69.982677  12.520880\n",
       "1     Afghanistan   66.004734  33.835231\n",
       "2          Angola   17.537368 -12.293361\n",
       "3        Anguilla  -63.064989  18.223959\n",
       "4         Albania   20.049834  41.142450\n",
       "..            ...         ...        ...\n",
       "236         Samoa -172.164851 -13.753243\n",
       "237         Yemen   47.586762  15.909280\n",
       "238  South Africa   25.083901 -29.000341\n",
       "239        Zambia   27.774759 -13.458242\n",
       "240      Zimbabwe   29.851441 -19.004204\n",
       "\n",
       "[241 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "data = pd.read_csv('Countries_exercise.csv')\n",
    "# Check the data\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the <i>'Longtitude'</i> and <i>'Latitude'</i> columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Longitude'], data['Latitude'])\n",
    "plt.xlim(-180,180)\n",
    "plt.ylim(-90, 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select the features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure to select the appropriate features since we are no longer using the categorical variable for our clustering but rather 'Longitude' and 'Laditude'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "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>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-69.982677</td>\n",
       "      <td>12.520880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>66.004734</td>\n",
       "      <td>33.835231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>17.537368</td>\n",
       "      <td>-12.293361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-63.064989</td>\n",
       "      <td>18.223959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>20.049834</td>\n",
       "      <td>41.142450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>236</td>\n",
       "      <td>-172.164851</td>\n",
       "      <td>-13.753243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>237</td>\n",
       "      <td>47.586762</td>\n",
       "      <td>15.909280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>238</td>\n",
       "      <td>25.083901</td>\n",
       "      <td>-29.000341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239</td>\n",
       "      <td>27.774759</td>\n",
       "      <td>-13.458242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>240</td>\n",
       "      <td>29.851441</td>\n",
       "      <td>-19.004204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Longitude   Latitude\n",
       "0    -69.982677  12.520880\n",
       "1     66.004734  33.835231\n",
       "2     17.537368 -12.293361\n",
       "3    -63.064989  18.223959\n",
       "4     20.049834  41.142450\n",
       "..          ...        ...\n",
       "236 -172.164851 -13.753243\n",
       "237   47.586762  15.909280\n",
       "238   25.083901 -29.000341\n",
       "239   27.774759 -13.458242\n",
       "240   29.851441 -19.004204\n",
       "\n",
       "[241 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data.iloc[:,1:3]\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use 4 clusters initially. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(4)"
   ]
  },
  {
   "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=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering Resutls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 3, 3, 3, 1, 0, 1, 0, 1, 2, 1, 0, 2, 3, 1, 1, 3, 3, 3,\n",
       "       2, 3, 1, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 2, 2, 1, 1, 3, 0, 2, 3, 1,\n",
       "       1, 1, 0, 0, 1, 3, 0, 0, 0, 0, 3, 3, 3, 3, 1, 0, 3, 0, 3, 0, 1, 1,\n",
       "       3, 3, 1, 3, 2, 0, 3, 3, 2, 1, 3, 1, 3, 3, 3, 3, 3, 1, 3, 0, 3, 0,\n",
       "       2, 0, 2, 1, 0, 3, 0, 3, 2, 3, 1, 2, 1, 3, 1, 1, 3, 1, 3, 0, 3, 1,\n",
       "       2, 1, 1, 1, 1, 2, 0, 0, 2, 3, 1, 2, 1, 3, 3, 0, 3, 1, 1, 3, 3, 3,\n",
       "       2, 0, 3, 3, 3, 1, 1, 0, 2, 3, 3, 3, 2, 3, 2, 2, 1, 3, 0, 1, 1, 2,\n",
       "       1, 2, 3, 2, 3, 0, 0, 3, 3, 1, 2, 2, 1, 1, 0, 0, 0, 2, 2, 2, 3, 0,\n",
       "       2, 3, 0, 1, 0, 1, 3, 2, 1, 3, 1, 1, 1, 3, 2, 0, 3, 2, 3, 0, 3, 1,\n",
       "       1, 0, 3, 1, 0, 3, 3, 3, 1, 0, 1, 1, 0, 1, 3, 2, 1, 1, 2, 0, 0, 3,\n",
       "       3, 2, 1, 1, 3, 0, 0, 1, 3, 0, 0, 0, 0, 2, 2, 0, 0, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identified_clusters = kmeans.fit_predict(x)\n",
    "identified_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>name</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>-69.982677</td>\n",
       "      <td>12.520880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>66.004734</td>\n",
       "      <td>33.835231</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Angola</td>\n",
       "      <td>17.537368</td>\n",
       "      <td>-12.293361</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Anguilla</td>\n",
       "      <td>-63.064989</td>\n",
       "      <td>18.223959</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Albania</td>\n",
       "      <td>20.049834</td>\n",
       "      <td>41.142450</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>236</td>\n",
       "      <td>Samoa</td>\n",
       "      <td>-172.164851</td>\n",
       "      <td>-13.753243</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>237</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>47.586762</td>\n",
       "      <td>15.909280</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>238</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>25.083901</td>\n",
       "      <td>-29.000341</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>27.774759</td>\n",
       "      <td>-13.458242</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>240</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>29.851441</td>\n",
       "      <td>-19.004204</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name   Longitude   Latitude  Cluster\n",
       "0           Aruba  -69.982677  12.520880        0\n",
       "1     Afghanistan   66.004734  33.835231        1\n",
       "2          Angola   17.537368 -12.293361        1\n",
       "3        Anguilla  -63.064989  18.223959        0\n",
       "4         Albania   20.049834  41.142450        3\n",
       "..            ...         ...        ...      ...\n",
       "236         Samoa -172.164851 -13.753243        0\n",
       "237         Yemen   47.586762  15.909280        1\n",
       "238  South Africa   25.083901 -29.000341        1\n",
       "239        Zambia   27.774759 -13.458242        1\n",
       "240      Zimbabwe   29.851441 -19.004204        1\n",
       "\n",
       "[241 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_clusters = data.copy()\n",
    "data_with_clusters['Cluster'] = identified_clusters\n",
    "data_with_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data once again. This time use the <i> c </i> parameter to separate the data by the clusters we defined.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Longitude'], data['Latitude'], c=data_with_clusters['Cluster'], cmap = 'rainbow')\n",
    "plt.xlim(-180,180)\n",
    "plt.ylim(-90, 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting the number of clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WCSS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the ingerated <i>sklearn</i> method <i> 'inertia_' </i>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256573.08675934302"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.inertia_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a loop that calculates and saves the WCSS for any number of clusters from 1 up to 10 (or more if you wish)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "wcss = []\n",
    "# 'cl_num' is a that keeps track the highest number of clusters we want to use the WCSS method for.\n",
    "# Note that 'range' doesn't include the upper boundery\n",
    "cl_num = 11\n",
    "for i in range (1,cl_num):\n",
    "    kmeans= KMeans(i)\n",
    "    kmeans.fit(x)\n",
    "    wcss_iter = kmeans.inertia_\n",
    "    wcss.append(wcss_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1413859.6798344273,\n",
       " 657012.0797586325,\n",
       " 325235.52237055893,\n",
       " 256573.08675934302,\n",
       " 192212.4818949704,\n",
       " 152256.35964587532,\n",
       " 127743.1607905005,\n",
       " 102891.39703473674,\n",
       " 84967.41720068289,\n",
       " 76845.18841695262]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Elbow Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Within-cluster Sum of Squares')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "number_clusters = range(1,cl_num)\n",
    "plt.plot(number_clusters, wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Within-cluster Sum of Squares')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the Elbow Curve, plot several graphs with the appropriate amount of clusters you believe would best fit the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this this specific case, that would be 2 or 3.\n",
    "Compare the scatter plots to determine which one to use in any further analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>Hint: we already created the scatter plot for 4 clusters, so we only have to slightly alter our code.</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(2)\n",
    "kmeans.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "identified_clusters = kmeans.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_clusters = data.copy()\n",
    "data_with_clusters['Cluster'] = identified_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Longitude'], data['Latitude'], c=data_with_clusters['Cluster'], cmap = 'rainbow')\n",
    "plt.xlim(-180,180)\n",
    "plt.ylim(-90, 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(3)\n",
    "kmeans.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "identified_clusters = kmeans.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_clusters = data.copy()\n",
    "data_with_clusters['Cluster'] = identified_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Longitude'], data['Latitude'], c=data_with_clusters['Cluster'], cmap = 'rainbow')\n",
    "plt.xlim(-180,180)\n",
    "plt.ylim(-90, 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Think about the geological developments of Earth and how it split from one big continent (Pangea) into smaller multiple smaller ones. "
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}