Assignment-9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "afc6ca90",
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1928152 Animesh Pathak Assignment-10\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "df1 = pd.read_csv('C:\\\\Users\\\\KIIT\\\\Downloads\\\\housing.csv~\\\\housing.csv')\n",
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f0cbe06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        207\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "median_house_value      0\n",
       "ocean_proximity         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ddf135ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X= pd.DataFrame(np.c_[df1['longitude']],\n",
    "                columns=['longitude'])\n",
    "Y= df1['latitude']\n",
    "\n",
    "sns.scatterplot(data=df1,x='longitude',y='latitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41ce83de",
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.476125</td>\n",
       "      <td>0.328572</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "      <td>0.554893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.199555</td>\n",
       "      <td>0.226988</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "      <td>0.652300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.253984</td>\n",
       "      <td>0.147715</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.583665</td>\n",
       "      <td>0.182784</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.631474</td>\n",
       "      <td>0.549416</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean       0.476125      0.328572           28.639486   2635.763081   \n",
       "std        0.199555      0.226988           12.585558   2181.615252   \n",
       "min        0.000000      0.000000            1.000000      2.000000   \n",
       "25%        0.253984      0.147715           18.000000   1447.750000   \n",
       "50%        0.583665      0.182784           29.000000   2127.000000   \n",
       "75%        0.631474      0.549416           37.000000   3148.000000   \n",
       "max        1.000000      1.000000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value       cluster  \n",
       "count        20640.000000  20640.000000  \n",
       "mean        206855.816909      0.554893  \n",
       "std         115395.615874      0.652300  \n",
       "min          14999.000000      0.000000  \n",
       "25%         119600.000000      0.000000  \n",
       "50%         179700.000000      0.000000  \n",
       "75%         264725.000000      1.000000  \n",
       "max         500001.000000      2.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=3)\n",
    "y_predicted = km.fit_predict(df1[['longitude','latitude']])\n",
    "y_predicted\n",
    "df1['cluster']=y_predicted\n",
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d81c04da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.64390096, 0.13641102],\n",
       "       [0.24109419, 0.59225602],\n",
       "       [0.4628538 , 0.36699759]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "08819a6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        0.211155\n",
      "1        0.212151\n",
      "2        0.210159\n",
      "3        0.209163\n",
      "4        0.209163\n",
      "           ...   \n",
      "20635    0.324701\n",
      "20636    0.312749\n",
      "20637    0.311753\n",
      "20638    0.301793\n",
      "20639    0.309761\n",
      "Name: longitude, Length: 20640, dtype: float64\n",
      "0        0.567481\n",
      "1        0.565356\n",
      "2        0.564293\n",
      "3        0.564293\n",
      "4        0.564293\n",
      "           ...   \n",
      "20635    0.737513\n",
      "20636    0.738576\n",
      "20637    0.732200\n",
      "20638    0.732200\n",
      "20639    0.725824\n",
      "Name: latitude, Length: 20640, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(df1[['longitude']])\n",
    "df1['longitude'] = scaler.transform(df1[['longitude']])\n",
    "\n",
    "scaler.fit(df1[['latitude']])\n",
    "df1['latitude'] = scaler.transform(df1[['latitude']])\n",
    "\n",
    "print(df1['longitude'])\n",
    "print(df1['latitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43dfc23e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17dc0fbf5e0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = df1[df1.cluster==0]\n",
    "df3 = df1[df1.cluster==1]\n",
    "df4 = df1[df1.cluster==2]\n",
    "plt.scatter(df2.longitude,df2['latitude'],color='red')\n",
    "plt.scatter(df3.longitude,df3['latitude'],color='pink')\n",
    "plt.scatter(df4.longitude,df4['latitude'],color='orange')\n",
    "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='cyan',marker='*',label='centroid')\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('latitude')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86b6b071",
   "metadata": {},
   "outputs": [],
   "source": [
    "sse = []\n",
    "k_rng = range(1,10)\n",
    "for k in k_rng:\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km.fit(df1[['longitude','latitude']])\n",
    "    sse.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "72acd8d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17dc1179760>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('K')\n",
    "plt.ylabel('Sum of squared error')\n",
    "plt.plot(k_rng,sse)"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}