kmeans-clustering countries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(r'D:\\Stu\\Data Science\\Exercises Regression\\3.01. Country clusters.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USA</td>\n",
       "      <td>44.97</td>\n",
       "      <td>-103.77</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada</td>\n",
       "      <td>62.40</td>\n",
       "      <td>-96.80</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>46.75</td>\n",
       "      <td>2.40</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UK</td>\n",
       "      <td>54.01</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>51.15</td>\n",
       "      <td>10.40</td>\n",
       "      <td>German</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Australia</td>\n",
       "      <td>-25.45</td>\n",
       "      <td>133.11</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  Latitude  Longitude Language\n",
       "0        USA     44.97    -103.77  English\n",
       "1     Canada     62.40     -96.80  English\n",
       "2     France     46.75       2.40   French\n",
       "3         UK     54.01      -2.53  English\n",
       "4    Germany     51.15      10.40   German\n",
       "5  Australia    -25.45     133.11  English"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 7,
     "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": [
    "plt.scatter(data['Longitude'],data['Latitude'])\n",
    "plt.xlim(-160,160)\n",
    "plt.ylim(-120,120)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.97</td>\n",
       "      <td>-103.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62.40</td>\n",
       "      <td>-96.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46.75</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54.01</td>\n",
       "      <td>-2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.15</td>\n",
       "      <td>10.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-25.45</td>\n",
       "      <td>133.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Latitude  Longitude\n",
       "0     44.97    -103.77\n",
       "1     62.40     -96.80\n",
       "2     46.75       2.40\n",
       "3     54.01      -2.53\n",
       "4     51.15      10.40\n",
       "5    -25.45     133.11"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=data.iloc[:,1:3]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mention the number of clusters you want\n",
    "kmeans=KMeans(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identified_clusters=kmeans.fit_predict(x)\n",
    "identified_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Language</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USA</td>\n",
       "      <td>44.97</td>\n",
       "      <td>-103.77</td>\n",
       "      <td>English</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada</td>\n",
       "      <td>62.40</td>\n",
       "      <td>-96.80</td>\n",
       "      <td>English</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>46.75</td>\n",
       "      <td>2.40</td>\n",
       "      <td>French</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UK</td>\n",
       "      <td>54.01</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>English</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>51.15</td>\n",
       "      <td>10.40</td>\n",
       "      <td>German</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Australia</td>\n",
       "      <td>-25.45</td>\n",
       "      <td>133.11</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  Latitude  Longitude Language  Cluster\n",
       "0        USA     44.97    -103.77  English        2\n",
       "1     Canada     62.40     -96.80  English        2\n",
       "2     France     46.75       2.40   French        0\n",
       "3         UK     54.01      -2.53  English        0\n",
       "4    Germany     51.15      10.40   German        0\n",
       "5  Australia    -25.45     133.11  English        1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_clusters=data.copy()\n",
    "data_with_clusters['Cluster']=identified_clusters\n",
    "data_with_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 18,
     "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": [
    "plt.scatter(data_with_clusters['Longitude'],data_with_clusters['Latitude'],c=data_with_clusters['Cluster'],cmap='rainbow')\n",
    "plt.xlim(-160,160)\n",
    "plt.ylim(-120,120)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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