k-means clustering - Medium.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-means clustering on housing prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unimportant stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing libraries and loading dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load our dataset from the sklearn library. In addition, we also download numpy and pandas to explore our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = datasets.load_boston()\n",
    "df = pd.DataFrame(boston.data,columns=boston.feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 13 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null float64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       506 non-null float64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null float64\n",
      "TAX        506 non-null float64\n",
      "PTRATIO    506 non-null float64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 51.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.shape\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.593761</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.596783</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.647423</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.593761   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.596783   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.647423   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT  \n",
       "count  506.000000  \n",
       "mean    12.653063  \n",
       "std      7.141062  \n",
       "min      1.730000  \n",
       "25%      6.950000  \n",
       "50%     11.360000  \n",
       "75%     16.955000  \n",
       "max     37.970000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing libraries required for clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Kmeans = cluster.KMeans(n_clusters = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = Kmeans.fit_predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c25e588>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1c2ad6a0>"
      ]
     },
     "execution_count": 60,
     "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.subplot(111)\n",
    "plt.scatter(data[:,5],data[:,6],c=y)\n"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}