kmeans-categorical(continents) .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>-69.982677</td>\n",
       "      <td>12.520880</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>66.004734</td>\n",
       "      <td>33.835231</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>17.537368</td>\n",
       "      <td>-12.293361</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>-63.064989</td>\n",
       "      <td>18.223959</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>20.049834</td>\n",
       "      <td>41.142450</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>-172.164851</td>\n",
       "      <td>-13.753243</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>47.586762</td>\n",
       "      <td>15.909280</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>25.083901</td>\n",
       "      <td>-29.000341</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>27.774759</td>\n",
       "      <td>-13.458242</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>29.851441</td>\n",
       "      <td>-19.004204</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name   Longitude   Latitude      continent\n",
       "0           Aruba  -69.982677  12.520880  North America\n",
       "1     Afghanistan   66.004734  33.835231           Asia\n",
       "2          Angola   17.537368 -12.293361         Africa\n",
       "3        Anguilla  -63.064989  18.223959  North America\n",
       "4         Albania   20.049834  41.142450         Europe\n",
       "..            ...         ...        ...            ...\n",
       "236         Samoa -172.164851 -13.753243        Oceania\n",
       "237         Yemen   47.586762  15.909280           Asia\n",
       "238  South Africa   25.083901 -29.000341         Africa\n",
       "239        Zambia   27.774759 -13.458242         Africa\n",
       "240      Zimbabwe   29.851441 -19.004204         Africa\n",
       "\n",
       "[241 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "raw_data = pd.read_csv(r'D:\\Stu\\Data Science\\Exercises Regression\\Categorical.csv')\n",
    "# Check the data\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = raw_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>-69.982677</td>\n",
       "      <td>12.520880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>66.004734</td>\n",
       "      <td>33.835231</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>17.537368</td>\n",
       "      <td>-12.293361</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>-63.064989</td>\n",
       "      <td>18.223959</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>20.049834</td>\n",
       "      <td>41.142450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>-172.164851</td>\n",
       "      <td>-13.753243</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>47.586762</td>\n",
       "      <td>15.909280</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>25.083901</td>\n",
       "      <td>-29.000341</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>27.774759</td>\n",
       "      <td>-13.458242</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>29.851441</td>\n",
       "      <td>-19.004204</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name   Longitude   Latitude  continent\n",
       "0           Aruba  -69.982677  12.520880          0\n",
       "1     Afghanistan   66.004734  33.835231          2\n",
       "2          Angola   17.537368 -12.293361          3\n",
       "3        Anguilla  -63.064989  18.223959          0\n",
       "4         Albania   20.049834  41.142450          1\n",
       "..            ...         ...        ...        ...\n",
       "236         Samoa -172.164851 -13.753243          5\n",
       "237         Yemen   47.586762  15.909280          2\n",
       "238  South Africa   25.083901 -29.000341          3\n",
       "239        Zambia   27.774759 -13.458242          3\n",
       "240      Zimbabwe   29.851441 -19.004204          3\n",
       "\n",
       "[241 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mapped = data.copy()\n",
    "data_mapped['continent'] = data_mapped['continent'].map({'North America':0,'Europe':1,'Asia':2,'Africa':3,'South America':4, 'Oceania':5,'Seven seas (open ocean)':6, 'Antarctica':7})\n",
    "data_mapped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data_mapped.iloc[:,3:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=7, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(7)\n",
    "kmeans.fit(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 3, 2, 4, 4, 4, 0, 5, 0, 1, 6, 1, 6, 2, 1, 4, 0, 3, 4, 3, 3,\n",
       "       0, 4, 0, 2, 2, 4, 2, 4, 2, 2, 5, 5, 2, 0, 0, 3, 3, 4, 5, 0, 3, 3,\n",
       "       3, 3, 1, 5, 3, 3, 2, 2, 2, 2, 0, 0, 4, 4, 3, 2, 4, 2, 3, 5, 3, 3,\n",
       "       4, 4, 3, 4, 1, 5, 4, 4, 1, 3, 4, 0, 4, 3, 3, 3, 3, 3, 4, 2, 2, 2,\n",
       "       1, 5, 0, 6, 2, 4, 2, 4, 0, 4, 0, 0, 6, 4, 0, 0, 4, 0, 4, 2, 4, 0,\n",
       "       0, 0, 0, 3, 0, 0, 1, 2, 0, 4, 0, 0, 0, 3, 3, 2, 4, 0, 3, 4, 4, 4,\n",
       "       0, 2, 3, 4, 4, 3, 6, 2, 1, 4, 3, 4, 0, 4, 0, 1, 3, 3, 2, 6, 3, 0,\n",
       "       3, 1, 3, 1, 3, 2, 1, 4, 4, 0, 1, 1, 0, 0, 2, 1, 5, 0, 1, 1, 4, 2,\n",
       "       0, 4, 5, 0, 1, 0, 4, 4, 3, 3, 0, 3, 3, 3, 0, 6, 6, 1, 3, 2, 4, 3,\n",
       "       3, 2, 4, 3, 5, 4, 4, 4, 3, 2, 6, 0, 2, 3, 3, 0, 0, 0, 0, 1, 2, 3,\n",
       "       0, 0, 3, 3, 4, 5, 2, 0, 4, 2, 5, 2, 2, 0, 1, 1, 1, 0, 3, 3, 3])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identified_clusters = kmeans.fit_predict(x)\n",
    "identified_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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>continent</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>-69.982677</td>\n",
       "      <td>12.520880</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>66.004734</td>\n",
       "      <td>33.835231</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>17.537368</td>\n",
       "      <td>-12.293361</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>-63.064989</td>\n",
       "      <td>18.223959</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>20.049834</td>\n",
       "      <td>41.142450</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>-172.164851</td>\n",
       "      <td>-13.753243</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>47.586762</td>\n",
       "      <td>15.909280</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>25.083901</td>\n",
       "      <td>-29.000341</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>27.774759</td>\n",
       "      <td>-13.458242</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>29.851441</td>\n",
       "      <td>-19.004204</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name   Longitude   Latitude  continent  Cluster\n",
       "0           Aruba  -69.982677  12.520880          0        2\n",
       "1     Afghanistan   66.004734  33.835231          2        0\n",
       "2          Angola   17.537368 -12.293361          3        3\n",
       "3        Anguilla  -63.064989  18.223959          0        2\n",
       "4         Albania   20.049834  41.142450          1        4\n",
       "..            ...         ...        ...        ...      ...\n",
       "236         Samoa -172.164851 -13.753243          5        1\n",
       "237         Yemen   47.586762  15.909280          2        0\n",
       "238  South Africa   25.083901 -29.000341          3        3\n",
       "239        Zambia   27.774759 -13.458242          3        3\n",
       "240      Zimbabwe   29.851441 -19.004204          3        3\n",
       "\n",
       "[241 rows x 5 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_clusters = data_mapped.copy()\n",
    "data_with_clusters['Cluster'] = identified_clusters\n",
    "data_with_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "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(-260,260)\n",
    "plt.ylim(-150, 150)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}