assignment dbscan crime data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import silhouette_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         city  Murder  Assault  UrbanPop  Rape\n",
       "0     Alabama    13.2      236        58  21.2\n",
       "1      Alaska    10.0      263        48  44.5\n",
       "2     Arizona     8.1      294        80  31.0\n",
       "3    Arkansas     8.8      190        50  19.5\n",
       "4  California     9.0      276        91  40.6"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('crime_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   city      50 non-null     object \n",
      " 1   Murder    50 non-null     float64\n",
      " 2   Assault   50 non-null     int64  \n",
      " 3   UrbanPop  50 non-null     int64  \n",
      " 4   Rape      50 non-null     float64\n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.78800</td>\n",
       "      <td>170.760000</td>\n",
       "      <td>65.540000</td>\n",
       "      <td>21.232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.35551</td>\n",
       "      <td>83.337661</td>\n",
       "      <td>14.474763</td>\n",
       "      <td>9.366385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.80000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.07500</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>15.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.25000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>20.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.25000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>77.750000</td>\n",
       "      <td>26.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.40000</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Murder     Assault   UrbanPop       Rape\n",
       "count  50.00000   50.000000  50.000000  50.000000\n",
       "mean    7.78800  170.760000  65.540000  21.232000\n",
       "std     4.35551   83.337661  14.474763   9.366385\n",
       "min     0.80000   45.000000  32.000000   7.300000\n",
       "25%     4.07500  109.000000  54.500000  15.075000\n",
       "50%     7.25000  159.000000  66.000000  20.100000\n",
       "75%    11.25000  249.000000  77.750000  26.175000\n",
       "max    17.40000  337.000000  91.000000  46.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Murder  Assault  UrbanPop  Rape\n",
       "0    13.2      236        58  21.2\n",
       "1    10.0      263        48  44.5\n",
       "2     8.1      294        80  31.0\n",
       "3     8.8      190        50  19.5\n",
       "4     9.0      276        91  40.6"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=data.drop(['city'],axis=1)\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.746988</td>\n",
       "      <td>0.654110</td>\n",
       "      <td>0.440678</td>\n",
       "      <td>0.359173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.554217</td>\n",
       "      <td>0.746575</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>0.961240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.439759</td>\n",
       "      <td>0.852740</td>\n",
       "      <td>0.813559</td>\n",
       "      <td>0.612403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.481928</td>\n",
       "      <td>0.496575</td>\n",
       "      <td>0.305085</td>\n",
       "      <td>0.315245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.493976</td>\n",
       "      <td>0.791096</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.860465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "          0         1         2         3\n",
       "0  0.746988  0.654110  0.440678  0.359173\n",
       "1  0.554217  0.746575  0.271186  0.961240\n",
       "2  0.439759  0.852740  0.813559  0.612403\n",
       "3  0.481928  0.496575  0.305085  0.315245\n",
       "4  0.493976  0.791096  1.000000  0.860465"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale=MinMaxScaler()\n",
    "data2=pd.DataFrame(scale.fit_transform(data1))\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "dbscan=DBSCAN(eps=0.308,min_samples=5).fit(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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       "      dtype=int64)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>cluster</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         city  Murder  Assault  UrbanPop  Rape  cluster\n",
       "0     Alabama    13.2      236        58  21.2        0\n",
       "1      Alaska    10.0      263        48  44.5       -1\n",
       "2     Arizona     8.1      294        80  31.0        1\n",
       "3    Arkansas     8.8      190        50  19.5        2\n",
       "4  California     9.0      276        91  40.6       -1"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['cluster']=dbscan.labels_\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Murder', ylabel='Rape'>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Murder',y='Rape',c=dbscan.labels_,s=50,kind=\"scatter\",cmap=plt.cm.coolwarm_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2287158308162511"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(data2,dbscan.labels_)"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}