dbscan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DBSCAN Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DBSCAN Algorithm**\n",
    "<pre>\n",
    "For each object p in D\n",
    "    if p is a core object and not processed then \n",
    "        C = retrieve all objects density-reachable from p \n",
    "        mark all objects in C as processed\n",
    "        report C as a cluster\n",
    "    else mark p as outlier\n",
    "    end if\n",
    "End For\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DBSCAN(object):\n",
    "    UN_CLASSIFIED = 0\n",
    "    NOISE = -1\n",
    "\n",
    "    UN_VISITED = 0\n",
    "    VISITED = 1\n",
    "\n",
    "    def __init__(self, eps, min_pts):\n",
    "        self.eps = eps\n",
    "        self.minPts = min_pts\n",
    "        self.n_cluster = 1\n",
    "\n",
    "    def fit(self, X):\n",
    "        self._train(X)\n",
    "\n",
    "    def _train(self, X):\n",
    "        n = X.shape[0]\n",
    "        self.labels_ = np.array([DBSCAN.UN_CLASSIFIED] * n)\n",
    "        visited = DBSCAN.UN_VISITED * np.ones(n)\n",
    "\n",
    "        left_classified, = np.where(visited == DBSCAN.UN_VISITED)\n",
    "        while len(left_classified) != 0:\n",
    "            x_idx = left_classified[0]\n",
    "\n",
    "            visited[x_idx] = DBSCAN.VISITED # mark visited\n",
    "            start_point = X[x_idx, :]\n",
    "            n_neighbors, pts = self.get_neighbors(X, start_point)\n",
    "\n",
    "            if n_neighbors < self.minPts:\n",
    "                self.labels_[x_idx] = DBSCAN.NOISE\n",
    "            else:\n",
    "                self.labels_[x_idx] = self.n_cluster\n",
    "                while len(pts) != 0:\n",
    "                    neighor_id = pts[0]\n",
    "                    pts = np.delete(pts, np.where(pts == neighor_id))\n",
    "                    if visited[neighor_id] == DBSCAN.UN_VISITED:\n",
    "                        visited[neighor_id] = DBSCAN.VISITED # mark visited\n",
    "                        n_new_neighors, new_pts = self.get_neighbors(X, X[neighor_id])\n",
    "\n",
    "                        if n_new_neighors >= self.minPts:\n",
    "                            pts = np.concatenate((pts, new_pts), axis=None)\n",
    "\n",
    "                    if self.labels_[neighor_id] == DBSCAN.UN_CLASSIFIED:\n",
    "                        self.labels_[neighor_id] = self.n_cluster\n",
    "\n",
    "                self.n_cluster += 1\n",
    "\n",
    "            left_classified, = np.where(visited == DBSCAN.UN_VISITED)\n",
    "\n",
    "    def get_neighbors(self, X, pt):\n",
    "        dist = np.linalg.norm(X - pt, 2, axis=1)\n",
    "        neighors = np.argsort(dist)\n",
    "        count, = np.where(dist <= self.eps)\n",
    "        return len(count), neighors[:len(count)]\n",
    "    \n",
    "    @staticmethod\n",
    "    def k_nearest_neighbour(X, pt, k):\n",
    "        dist = np.linalg.norm(X - pt, 2, axis=1)\n",
    "        neighors = np.argsort(dist)\n",
    "        k_dist = dist[neighors[k]]\n",
    "        return k_dist\n",
    "\n",
    "    @classmethod\n",
    "    def plot_param_est_curve(cls, data, k_nn):\n",
    "        y = sorted([DBSCAN.k_nearest_neighbour(data, pt, k_nn) for pt in data])\n",
    "        # plot\n",
    "        plt.plot([id_ for id_ in range(len(data))], y, marker='o')\n",
    "        plt.xlabel('Points sorted according to k={} nearest neighour'.format(k_nn))\n",
    "        plt.ylabel('k={} Nearest Distance'.format(k_nn))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>murder</th>\n",
       "      <th>rape</th>\n",
       "      <th>foeticide</th>\n",
       "      <th>kidnapping and abduction</th>\n",
       "      <th>abetment of suicide</th>\n",
       "      <th>exposure and abandonment</th>\n",
       "      <th>procuration of minor girls</th>\n",
       "      <th>selling girls for prostitution</th>\n",
       "      <th>prohibition of child marriage act</th>\n",
       "      <th>other crimes</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>andhra pradesh</td>\n",
       "      <td>2001</td>\n",
       "      <td>35.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arunachal pradesh</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>assam</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bihar</td>\n",
       "      <td>2001</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chhattisgarh</td>\n",
       "      <td>2001</td>\n",
       "      <td>14.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>d&amp;n haveli</td>\n",
       "      <td>2015</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>daman &amp; diu</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2015</td>\n",
       "      <td>54.0</td>\n",
       "      <td>927.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>9489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>lakshadweep</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>puducherry</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>527 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 state  year  murder   rape  foeticide  \\\n",
       "0       andhra pradesh  2001    35.0   84.0        0.0   \n",
       "1    arunachal pradesh  2001     0.0    0.0        0.0   \n",
       "2                assam  2001     0.0    0.0        0.0   \n",
       "3                bihar  2001     2.0   16.0        0.0   \n",
       "4         chhattisgarh  2001    14.0  150.0        5.0   \n",
       "..                 ...   ...     ...    ...        ...   \n",
       "522         d&n haveli  2015     2.0    2.0        0.0   \n",
       "523        daman & diu  2015     1.0    0.0        0.0   \n",
       "524              delhi  2015    54.0  927.0        3.0   \n",
       "525        lakshadweep  2015     0.0    0.0        0.0   \n",
       "526         puducherry  2015     0.0    0.0        0.0   \n",
       "\n",
       "     kidnapping and abduction  abetment of suicide  exposure and abandonment  \\\n",
       "0                        57.0                  7.0                      22.0   \n",
       "1                         0.0                  0.0                       0.0   \n",
       "2                        18.0                  0.0                       0.0   \n",
       "3                        26.0                  0.0                       1.0   \n",
       "4                        46.0                  1.0                      15.0   \n",
       "..                        ...                  ...                       ...   \n",
       "522                      14.0                  0.0                       2.0   \n",
       "523                      21.0                  0.0                       0.0   \n",
       "524                    6881.0                  0.0                      23.0   \n",
       "525                       0.0                  0.0                       0.0   \n",
       "526                       5.0                  0.0                       0.0   \n",
       "\n",
       "     procuration of minor girls  selling girls for prostitution  \\\n",
       "0                          12.0                             0.0   \n",
       "1                           0.0                             0.0   \n",
       "2                           0.0                             0.0   \n",
       "3                          16.0                             1.0   \n",
       "4                           0.0                             0.0   \n",
       "..                          ...                             ...   \n",
       "522                         0.0                             0.0   \n",
       "523                         0.0                             0.0   \n",
       "524                         0.0                             6.0   \n",
       "525                         0.0                             0.0   \n",
       "526                         0.0                             0.0   \n",
       "\n",
       "     prohibition of child marriage act  other crimes   total  \n",
       "0                                  6.0          47.0   270.0  \n",
       "1                                  0.0           0.0     0.0  \n",
       "2                                  0.0           0.0    18.0  \n",
       "3                                  2.0          18.0    83.0  \n",
       "4                                  0.0         354.0   585.0  \n",
       "..                                 ...           ...     ...  \n",
       "522                                0.0           2.0    35.0  \n",
       "523                                0.0           0.0    28.0  \n",
       "524                                0.0         231.0  9489.0  \n",
       "525                                0.0           1.0     2.0  \n",
       "526                                0.0           2.0    56.0  \n",
       "\n",
       "[527 rows x 13 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_pickle('./state_clubbed_df.pickle')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['state', 'year', 'murder', 'rape', 'foeticide',\n",
       "       'kidnapping and abduction', 'abetment of suicide',\n",
       "       'exposure and abandonment', 'procuration of minor girls',\n",
       "       'selling girls for prostitution', 'prohibition of child marriage act',\n",
       "       'other crimes', 'total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_headers = data.columns\n",
    "all_headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['murder', 'rape', 'foeticide', 'kidnapping and abduction',\n",
      "       'abetment of suicide', 'exposure and abandonment',\n",
      "       'procuration of minor girls', 'selling girls for prostitution',\n",
      "       'prohibition of child marriage act', 'other crimes'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "cols_to_norm = all_headers[2:-1]\n",
    "print (cols_to_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>murder</th>\n",
       "      <th>rape</th>\n",
       "      <th>foeticide</th>\n",
       "      <th>kidnapping and abduction</th>\n",
       "      <th>abetment of suicide</th>\n",
       "      <th>exposure and abandonment</th>\n",
       "      <th>procuration of minor girls</th>\n",
       "      <th>selling girls for prostitution</th>\n",
       "      <th>prohibition of child marriage act</th>\n",
       "      <th>other crimes</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>andhra pradesh</td>\n",
       "      <td>2001</td>\n",
       "      <td>-0.088951</td>\n",
       "      <td>-0.293383</td>\n",
       "      <td>-0.422895</td>\n",
       "      <td>-0.331227</td>\n",
       "      <td>0.899853</td>\n",
       "      <td>-0.024876</td>\n",
       "      <td>-0.085015</td>\n",
       "      <td>-0.183679</td>\n",
       "      <td>0.287960</td>\n",
       "      <td>-0.288916</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arunachal pradesh</td>\n",
       "      <td>2001</td>\n",
       "      <td>-0.518114</td>\n",
       "      <td>-0.556170</td>\n",
       "      <td>-0.422895</td>\n",
       "      <td>-0.391738</td>\n",
       "      <td>-0.281905</td>\n",
       "      <td>-0.435728</td>\n",
       "      <td>-0.216594</td>\n",
       "      <td>-0.183679</td>\n",
       "      <td>-0.442216</td>\n",
       "      <td>-0.386870</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>assam</td>\n",
       "      <td>2001</td>\n",
       "      <td>-0.518114</td>\n",
       "      <td>-0.556170</td>\n",
       "      <td>-0.422895</td>\n",
       "      <td>-0.372629</td>\n",
       "      <td>-0.281905</td>\n",
       "      <td>-0.435728</td>\n",
       "      <td>-0.216594</td>\n",
       "      <td>-0.183679</td>\n",
       "      <td>-0.442216</td>\n",
       "      <td>-0.386870</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bihar</td>\n",
       "      <td>2001</td>\n",
       "      <td>-0.493590</td>\n",
       "      <td>-0.506115</td>\n",
       "      <td>-0.422895</td>\n",
       "      <td>-0.364137</td>\n",
       "      <td>-0.281905</td>\n",
       "      <td>-0.417052</td>\n",
       "      <td>-0.041155</td>\n",
       "      <td>-0.092531</td>\n",
       "      <td>-0.198824</td>\n",
       "      <td>-0.349356</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chhattisgarh</td>\n",
       "      <td>2001</td>\n",
       "      <td>-0.346449</td>\n",
       "      <td>-0.086907</td>\n",
       "      <td>0.247176</td>\n",
       "      <td>-0.342905</td>\n",
       "      <td>-0.113082</td>\n",
       "      <td>-0.155602</td>\n",
       "      <td>-0.216594</td>\n",
       "      <td>-0.183679</td>\n",
       "      <td>-0.442216</td>\n",
       "      <td>0.350914</td>\n",
       "      <td>585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>d&amp;n haveli</td>\n",
       "      <td>2015</td>\n",
       "      <td>-0.493590</td>\n",
       "      <td>-0.549913</td>\n",
       "      <td>-0.422895</td>\n",
       "      <td>-0.376876</td>\n",
       "      <td>-0.281905</td>\n",
       "      <td>-0.398377</td>\n",
       "      <td>-0.216594</td>\n",
       "      <td>-0.183679</td>\n",
       "      <td>-0.442216</td>\n",
       "      <td>-0.382702</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>daman &amp; diu</td>\n",
       "      <td>2015</td>\n",
       "      <td>-0.505852</td>\n",
       "      <td>-0.556170</td>\n",
       "      <td>-0.422895</td>\n",
       "      <td>-0.369445</td>\n",
       "      <td>-0.281905</td>\n",
       "      <td>-0.435728</td>\n",
       "      <td>-0.216594</td>\n",
       "      <td>-0.183679</td>\n",
       "      <td>-0.442216</td>\n",
       "      <td>-0.386870</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.144024</td>\n",
       "      <td>2.343874</td>\n",
       "      <td>-0.020852</td>\n",
       "      <td>6.913125</td>\n",
       "      <td>-0.281905</td>\n",
       "      <td>-0.006201</td>\n",
       "      <td>-0.216594</td>\n",
       "      <td>0.363206</td>\n",
       "      <td>-0.442216</td>\n",
       "      <td>0.094565</td>\n",
       "      <td>9489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>lakshadweep</td>\n",
       "      <td>2015</td>\n",
       "      <td>-0.518114</td>\n",
       "      <td>-0.556170</td>\n",
       "      <td>-0.422895</td>\n",
       "      <td>-0.391738</td>\n",
       "      <td>-0.281905</td>\n",
       "      <td>-0.435728</td>\n",
       "      <td>-0.216594</td>\n",
       "      <td>-0.183679</td>\n",
       "      <td>-0.442216</td>\n",
       "      <td>-0.384786</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>puducherry</td>\n",
       "      <td>2015</td>\n",
       "      <td>-0.518114</td>\n",
       "      <td>-0.556170</td>\n",
       "      <td>-0.422895</td>\n",
       "      <td>-0.386430</td>\n",
       "      <td>-0.281905</td>\n",
       "      <td>-0.435728</td>\n",
       "      <td>-0.216594</td>\n",
       "      <td>-0.183679</td>\n",
       "      <td>-0.442216</td>\n",
       "      <td>-0.382702</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>527 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 state  year    murder      rape  foeticide  \\\n",
       "0       andhra pradesh  2001 -0.088951 -0.293383  -0.422895   \n",
       "1    arunachal pradesh  2001 -0.518114 -0.556170  -0.422895   \n",
       "2                assam  2001 -0.518114 -0.556170  -0.422895   \n",
       "3                bihar  2001 -0.493590 -0.506115  -0.422895   \n",
       "4         chhattisgarh  2001 -0.346449 -0.086907   0.247176   \n",
       "..                 ...   ...       ...       ...        ...   \n",
       "522         d&n haveli  2015 -0.493590 -0.549913  -0.422895   \n",
       "523        daman & diu  2015 -0.505852 -0.556170  -0.422895   \n",
       "524              delhi  2015  0.144024  2.343874  -0.020852   \n",
       "525        lakshadweep  2015 -0.518114 -0.556170  -0.422895   \n",
       "526         puducherry  2015 -0.518114 -0.556170  -0.422895   \n",
       "\n",
       "     kidnapping and abduction  abetment of suicide  exposure and abandonment  \\\n",
       "0                   -0.331227             0.899853                 -0.024876   \n",
       "1                   -0.391738            -0.281905                 -0.435728   \n",
       "2                   -0.372629            -0.281905                 -0.435728   \n",
       "3                   -0.364137            -0.281905                 -0.417052   \n",
       "4                   -0.342905            -0.113082                 -0.155602   \n",
       "..                        ...                  ...                       ...   \n",
       "522                 -0.376876            -0.281905                 -0.398377   \n",
       "523                 -0.369445            -0.281905                 -0.435728   \n",
       "524                  6.913125            -0.281905                 -0.006201   \n",
       "525                 -0.391738            -0.281905                 -0.435728   \n",
       "526                 -0.386430            -0.281905                 -0.435728   \n",
       "\n",
       "     procuration of minor girls  selling girls for prostitution  \\\n",
       "0                     -0.085015                       -0.183679   \n",
       "1                     -0.216594                       -0.183679   \n",
       "2                     -0.216594                       -0.183679   \n",
       "3                     -0.041155                       -0.092531   \n",
       "4                     -0.216594                       -0.183679   \n",
       "..                          ...                             ...   \n",
       "522                   -0.216594                       -0.183679   \n",
       "523                   -0.216594                       -0.183679   \n",
       "524                   -0.216594                        0.363206   \n",
       "525                   -0.216594                       -0.183679   \n",
       "526                   -0.216594                       -0.183679   \n",
       "\n",
       "     prohibition of child marriage act  other crimes   total  \n",
       "0                             0.287960     -0.288916   270.0  \n",
       "1                            -0.442216     -0.386870     0.0  \n",
       "2                            -0.442216     -0.386870    18.0  \n",
       "3                            -0.198824     -0.349356    83.0  \n",
       "4                            -0.442216      0.350914   585.0  \n",
       "..                                 ...           ...     ...  \n",
       "522                          -0.442216     -0.382702    35.0  \n",
       "523                          -0.442216     -0.386870    28.0  \n",
       "524                          -0.442216      0.094565  9489.0  \n",
       "525                          -0.442216     -0.384786     2.0  \n",
       "526                          -0.442216     -0.382702    56.0  \n",
       "\n",
       "[527 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cols_to_norm] = StandardScaler().fit_transform(data[cols_to_norm])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing Jupyter notebook from pca.ipynb\n"
     ]
    }
   ],
   "source": [
    "import nbimporter # library used for importing ipnb notebooks\n",
    "from pca import PCA ## import pca.ipynb as module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_features = len(cols_to_norm)\n",
    "n_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using PCA to determine the principal components of the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7fe85593c710>,\n",
       "  <matplotlib.axis.XTick at 0x7fe85593c6d0>,\n",
       "  <matplotlib.axis.XTick at 0x7fe8538f1790>,\n",
       "  <matplotlib.axis.XTick at 0x7fe85389c410>,\n",
       "  <matplotlib.axis.XTick at 0x7fe85389c8d0>,\n",
       "  <matplotlib.axis.XTick at 0x7fe85389ce10>,\n",
       "  <matplotlib.axis.XTick at 0x7fe8538aa3d0>,\n",
       "  <matplotlib.axis.XTick at 0x7fe8538aa910>,\n",
       "  <matplotlib.axis.XTick at 0x7fe8538aae50>,\n",
       "  <matplotlib.axis.XTick at 0x7fe8538b23d0>],\n",
       " <a list of 10 Text major ticklabel objects>)"
      ]
     },
     "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": [
    "pca = PCA(n_components=n_features)\n",
    "principal_components = pca.fit_transform(data[cols_to_norm].to_numpy())\n",
    "features = range(pca.n_components_)\n",
    "plt.bar(features, pca.explained_variance_ratio, color='black')\n",
    "plt.xlabel('PCA features')\n",
    "plt.ylabel('variance %')\n",
    "plt.xticks(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>andhra pradesh</td>\n",
       "      <td>2001</td>\n",
       "      <td>-0.228578</td>\n",
       "      <td>0.050406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arunachal pradesh</td>\n",
       "      <td>2001</td>\n",
       "      <td>-1.230578</td>\n",
       "      <td>-0.237921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>assam</td>\n",
       "      <td>2001</td>\n",
       "      <td>-1.224106</td>\n",
       "      <td>-0.236560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bihar</td>\n",
       "      <td>2001</td>\n",
       "      <td>-1.098062</td>\n",
       "      <td>0.019818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chhattisgarh</td>\n",
       "      <td>2001</td>\n",
       "      <td>-0.259834</td>\n",
       "      <td>-0.514825</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>522</th>\n",
       "      <td>d&amp;n haveli</td>\n",
       "      <td>2015</td>\n",
       "      <td>-1.199630</td>\n",
       "      <td>-0.241129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>daman &amp; diu</td>\n",
       "      <td>2015</td>\n",
       "      <td>-1.218726</td>\n",
       "      <td>-0.236485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2015</td>\n",
       "      <td>3.331256</td>\n",
       "      <td>0.160154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>lakshadweep</td>\n",
       "      <td>2015</td>\n",
       "      <td>-1.229735</td>\n",
       "      <td>-0.238179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>puducherry</td>\n",
       "      <td>2015</td>\n",
       "      <td>-1.227093</td>\n",
       "      <td>-0.238059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>527 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 state  year         0         1\n",
       "0       andhra pradesh  2001 -0.228578  0.050406\n",
       "1    arunachal pradesh  2001 -1.230578 -0.237921\n",
       "2                assam  2001 -1.224106 -0.236560\n",
       "3                bihar  2001 -1.098062  0.019818\n",
       "4         chhattisgarh  2001 -0.259834 -0.514825\n",
       "..                 ...   ...       ...       ...\n",
       "522         d&n haveli  2015 -1.199630 -0.241129\n",
       "523        daman & diu  2015 -1.218726 -0.236485\n",
       "524              delhi  2015  3.331256  0.160154\n",
       "525        lakshadweep  2015 -1.229735 -0.238179\n",
       "526         puducherry  2015 -1.227093 -0.238059\n",
       "\n",
       "[527 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PCA_components = pd.DataFrame(principal_components)\n",
    "PCA_components = pd.concat([data[all_headers[:2]], PCA_components.loc[:, [0,1]]], axis=1)\n",
    "PCA_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/pranjal/.local/share/virtualenvs/crime-analysis-report-pk3Cf-oK/lib/python3.7/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4bhVbtu3m4qt3dL1k5P4Ss2LwTO9FaLYKbTuNX6vk0u5qt532OyTFXk1kWZWMet1fYmaLk/soqUHTzk573f5k3emmTUmxD0mZlowGYbMps6XACaPL2mn8ur2Na/0OeYeuGObg5cu4+Ood8/pH2o19f0K3UbdKRt7Zz6wYXJLqsnb6HjauWzWv1AOdf7KulrEWM/IoKfbqqKl63SwZebMps/6nnAcdZWJiYiKmpqbyDiOVLOYfTE6X+OBX7mx4ZzA+OsJ3L3l9y69b+/rzRob5zVP7mNv/zPVGhod8F2A2QCRtj4iJpuc4YeQnq2RRf9dSS8Anz13T8JzRkWEuPau8+kr968PLxLOftZyZJ+c8sc5sAKVJGC5J5aTTyWpJyabRyKtaK0dHEs+ZmZ1j09adPGt4WcPlT1YctJzpj56+4H1mtjQ4YeSk1ZDaZncfzZJNs47oav/IxVfvSDxndm5/YsLph3kRXkLELD8eJZWTZkNqW80Wb5Zskjqil4kDfQ6L7azOe15E3iv4VmNoNDPfbClwwshJsyG1rZbKaJZsNq5btWCpdSjPpahqNHy21ujIcF/Oi8h7CZF+SFhmeXLCaFO3PmE2m6/RbMb12j+5keeNDDd8feXoCOvXjnPIQQsrjXNPx4GGtTrv4dAVC68zMjzEpWe9tC/nReS9hEjeCcssb+7DaEM3V1VtNl8jad4DwK+enGNomRheJuaenj/MtXoH8Phs493/ahvW6tf/+D/sOrBbYHWUVPW1LBJEJ30QeS8hknfCMsubE0YT9Y3bb/51X+qFBdNImqzWaEJfrf1PB89dMcyKg5Y3bHjTNKwfmdzJlbc9MG/58n/d93TLmDtp8DtNuFlMdGxH3gnLLG+eh5Gg1XyGeoKujtqZnC5xUZPRTALu3fzWxPc2aljf8Ypxbrl7b+LdCzwzsa+d66YtV526+eaGX7vZ12wUQ7/scw6ewGiDw/MwOtBqPkO92k5Q6Lyc06o01exTbaNy12knjHHN9lLL76lZeaWdpdvbuXY7JZ08lxBZzJLzZoPECSPBYuvS9Q1o/RIbEqlnS29ct4qNX71zXl8FwPCQ5pVhkj5111771M03p0qAyyQmp0sN4+q0wR+Eko7XvLKlzAkjQVLjlka1AZ2cLs1r8GdqOqPbWQzw0ut2HXjvoSuG+djbXjovIdX3C1x09Q4uvW7XgWU+mt2p1NsfkRhXpw1+L/ogPLGvP/jfYTA5YSQ47YQxvnjbA4t6b7UBvfS6XQvuDmqlKee0+kTbbJmPZn0gzSTF1WmDn6akk2enunWH/x0GlxNGA5PTJb70vcUli9oGdCZheGutTodkLvYuqJVGcXWjht8sAXba0HTax2Ld4X+HweWEUWdyusTGr91JkxuDeTpdxfV5I8OcuvnmVA3w5HRpXnkqS0llpixr+O3uh16fuDxPoj/432FwOWHU2bJt97x9H+qNDC/jqX3B/giGJM49+Sg+sX51w3MPXTF8YFJckpnZuQMJoDQzy8VX72Dq/sf4xPrVBxrF0swsEvRqBHSaMlMWNeq0DU2jO5GNX7sz8bpF6lQfBIMwuMEayzVhSDoD+EtgCPhsRGyue/1g4O+AVwC/BM6NiPuyiucjkztblnh+O/f0gclu+yO4+vYHmTjmMNavHecjkzsX9HsIaKedDzhwjdphsL1KFtVO9an7HzuwCdOQxPmvfCYxZlWjTtvQNLoTSUry9ckvKdG1mwDbOX+pdQDnPcFysZbav9Ni5DZxT9IQ8GPgTcBDwO3A+RHxw5pz/gg4MSIulHQe8PaIOLfVtRczca9RY5+nZSJ1Wayb1x2vzNlo9LP43VOO5hPrVydOwBuS+F/vOmnR/8nSTow77pLrUyXh+niaTWisn6PSbEJeOxP4Gs2oXwqT/YrW+HpSZv9P3DsZuCcifgYg6cvA2cAPa845G7i08vhrwKclKTLIcv2ULCCbZDFeWd6kWR/InpnZxJ/FF297gIljDkssHTUbkptG2k71tEOen46Y996kPpKrvvfggu1sm3XSpu1rmZwuLUgWra6dlV434EWbr+KO+nTyTBjjwIM1zx8CXpl0TkTsk/Q48G+AX9RfTNIGYAPA0Ucf3VYgr/zTm9o6v4iqy28ce8n1HV1n09adjDbpm5md28+l1+1quj94s8mLSQ1N/TWGh9S0rwkWlrKaJbpGSjOzHHfJ9QviTNvXsmXb7sQ7oV52AHuYa2vuqE9nYJY3j4jLI2IiIibGxsbaeu8jTzyVUVT947QTyj+T2n0xGml1YzM7t59fPTlHs6vMzM4dWPa9fg+Jmdk5fvXkXFv7STS6BlHubxHlv4eXzY+oUc08qdO12c+kGufFV+/gI5M7m14nbYJqdo0stLMse9E3iFps/Gn/TZe6PBNGCTiq5vmRlWMNz5G0HHge5c5va9M120tMTpcSP023q9VVqo1RqzW50uwn0bCTu7LH+L2b38rH3vZSDjn4mZvlQ1cMN6w9J+1Bcv4rj2q6oRSUv98rb3uAyekSp50wtiBhtpOgVImlV9odfVbUDaI6ib/Z/jT2jDwTxu3A8ZKOk3QQcB5wXd051wEXVB6/E7g5i/6LpaDaMI/36BNTaWY2sXO8Xqvb/jTb2db2y/x2rvEy7dWNo+o3hvrE+tXzjicJyrP3r9lempcwBbzjFQtLaY0aIQHvPuXonpaC0n56LvoGUZ3En/S74ZLdfLn1YVT6JP4Y2EZ5WO3nImKXpD8BpiLiOuAK4O8l3QM8Rjmp2CKVZmYZTditr9tE+lnoow12/quanC6xTGp4Z9RqO9uk4bKNllKv7TtplugaDRioDoW+5e698/o6+mV127TDXItex+80/qJ11Och13kYEXEDcEPdsY/WPP4t8Du9jmtQiYUN3orhZTyZ8Im8k6/T1tyThJOrdw+NkkW1wbs4Yb2s0swsaz5+Y6oFH+uTymknjDUc3dRKo+v3QyPU6eizotTxix5/EQxMp3cn7kvYiGiQJDXihx5ycMO9vRf9ddResoDkLWWT+j+GpAPlgmaNQaO7gfoSRaO69zXbS7z6hYc17KdYMdz8v0y/lnDWrx3nu5e8nns3v5XvXvL6xJ0ei1zHL3r8ReClQSraTRqNJmTVavdTdtaSYul2mSoi+XsfalJaaiSplFA7v6LVdratrptU0rrvl7N88tw1C+48rr79wfrLpY673/WyhJbFvJB+KQEOMieMRfrE+tWJa0jBM/8hSjOziQ2ogFe/8DDu++VsZqvOttKoTNWpYGHSaDajOukTYJoSQ30jkdTfkfT+pMa9NDPLlm275zU4p26+ueXcj/rrp9FPs6J7UULLcl5IP5QAB5kTRkZqf3Frk0f1U/Z4XcNQXSU3TYNUb0iwiLd1dBfUamHFoDzSpL4RnDjmsNSNY9rO2tqf9XEtJibWv7/ZrPH6hizNnUO7JZAiTarrVmLrp1nV/ZSsiyC3taSytJi1pPrB5HSJj//DrgMNcbVUlHQH0GhRwDSbJonOdhSsDg1ttkd4dWZ5p9r9D91shFP9boXV67cqaR26YpgVBy1PvO6QxNMRbW/B2yzebv38uqWbay0lrQUm4N4e9id6/aj5+n0tKavT6Ha6nV/q9WvHW27HWtsQpZ0nUS+AW+7ey2XnrJ6X4Grj61ZHY7slhkZ3JdUE16iEWFvSSvpZ/OrJucS7qeq/BdDwTmHq/se45e69iQmvKENZu3lX0C+jmfrpTqcoPEqqz7U7oWjjulULlsmoGl6meQ15o1Elw8t0YMmNZpP89szMsn7tONMfPZ1PnbumbyY8Nfp5ffLcNU37m6ojiNqd1Fj7vSY1Plfe9kDTmcdFWZKim4mtX0YzFSVZ9xPfYRRAO5+yq+fV78w3OjLMpWfNL8ekGVWSdBdS3/HcT5/I0sbTaP5FszJbLcG8klFSI9Nqpdqi7B3RzbuCfhnN1C93OkXqR3HCGEDtJphm5xalQWtXo87ma7aXeMcrxueVkJKWg69vVNrpE6pNLs0az35qSLr9e9APHzL64Xe7SIMewAnDWuiXT4PdllRCuuXuvfPuHJL6kOoblXbmg9QnmzR9V3k3JIP4e9AP31PR+lGcMKylfvg02G1p69dpG5Xq8+q2tknSfoLtx4ZkEH8P8v6eitaP4oRhS1I79eu0jcr6teOJa1sBC+beNNONhqSfSlrWWL/0o6TlUVK2JGU1UifpP3p1OHPaBrvT0VNF39tiqeiXEWNpOWHYkpTV/gfdagA6vU7R97ZYKoq2D4dLUrZkZVG/7lZHaqfXKVptfCnLux+lHU4YZl3WrQagk+sUrTZuxeCSlNkAKlpt3IrBdxhmA6gf5hjY4PEdhtkA8pBay4LvMMwGTL/NErfBkUvCkLQFeBvwFPBT4PcjYqbBefcBTwD7gX2t1mo3s/6cJd5rvsPKRl4lqZuAl0XEicCPgU1Nzj0tItY4WZils9SH1HrSYnZySRgRcWNE7Ks8vQ04Mo84zAZRUfbYyIonLWanHzq93wt8I+G1AG6UtF3ShmYXkbRB0pSkqb1793Y9SLOiWOpDapf6HVaWMuvDkPT/gOc3eOnDEfH1yjkfBvYBVyZc5jURUZL0b4GbJN0dEd9udGJEXA5cDuU9vTv+BswKaqkPqfWkxexkljAi4o3NXpf0HuBM4A0RjdeDjohS5e9HJV0LnAw0TBhm9owiLTfRbf2wMdKgyqUkJekM4EPAWRHxZMI5h0h6TvUxcDpwV++iNLMiKtqCfkWS1zyMTwMHUy4zAdwWERdKWgl8NiLeAhwBXFt5fTnwpYj4Zk7xmlmBLLU7rF4NI84lYUTEixKO7wHeUnn8M+CkXsZlZlY0vZyo2Q+jpMzMbJF6OYzYCcPMrMB6OYzYCcPMrMB6OVHTCcPMrMB6OVHTq9WamRVYLydqOmGYmRVcr4YRuyRlZmapOGGYmVkqThhmZpaKE4aZmaXihGFmZqkoYWXxQpO0F7g/7zjacDjwi7yD6ECR4y9y7OD481Tk2GFh/MdExFizNwxkwigaSVNF3rO8yPEXOXZw/HkqcuywuPhdkjIzs1ScMMzMLBUnjP5wed4BdKjI8Rc5dnD8eSpy7LCI+N2HYWZmqfgOw8zMUnHCMDOzVJwwcibpDEm7Jd0j6ZK840lL0lGSbpH0Q0m7JH0g75gWQ9KQpGlJ/5h3LO2SNCrpa5LulvQjSa/KO6a0JF1c+b25S9JVkp6Vd0zNSPqcpEcl3VVz7DBJN0n6SeXvQ/OMsZmE+LdUfnd+IOlaSaOtruOEkSNJQ8D/Bt4MvAQ4X9JL8o0qtX3AByPiJcApwPsLFHutDwA/yjuIRfpL4JsRcQJwEgX5PiSNA/8VmIiIlwFDwHn5RtXS54Ez6o5dAnwrIo4HvlV53q8+z8L4bwJeFhEnAj8GNrW6iBNGvk4G7omIn0XEU8CXgbNzjimViHg4Iu6oPH6CcmOV/YL8XSTpSOCtwGfzjqVdkp4HvBa4AiAinoqImXyjastyYETScmAFsCfneJqKiG8Dj9UdPhv4QuXxF4D1PQ2qDY3ij4gbI2Jf5eltwJGtruOEka9x4MGa5w9RsEYXQNKxwFrge/lG0rZPAR8Cns47kEU4DtgL/N9KSe2zkg7JO6g0IqIE/DnwAPAw8HhE3JhvVItyREQ8XHn8c+CIPIPp0HuBb7Q6yQnDOiLp2cA1wEUR8eu840lL0pnAoxGxPe9YFmk58HLgbyNiLfAb+rskckCl1n825aS3EjhE0u/mG1Vnojw/oZBzFCR9mHKJ+cpW5zph5KsEHFXz/MjKsUKQNEw5WVwZEVvzjqdNpwJnSbqPcinw9ZK+mG9IbXkIeCgiqnd1X6OcQIrgjcC9EbE3IuaArcCrc45pMR6R9AKAyt+P5hxP2yS9BzgTeHekmJTnhJGv24HjJR0n6SDKHX/X5RxTKpJEuX7+o4j4i7zjaVdEbIqIIyPiWMo/95sjojCfciPi58CDklZVDr0B+GGOIbXjAeAUSSsqv0dvoCAd9nWuAy6oPL4A+HqOsbRN0hmUS7JnRcSTad7jhJGjSofTHwPbKP+H+UpE7Mo3qtROBX6P8ifzHZU/b8k7qCXmvwBXSvoBsAb4s5zjSaVyV/Q14A5gJ+V2qK+X2ZB0FVU3pIkAAAHISURBVPDPwCpJD0l6H7AZeJOkn1C+a9qcZ4zNJMT/aeA5wE2V/7+faXkdLw1iZmZp+A7DzMxSccIwM7NUnDDMzCwVJwwzM0vFCcPMzFJxwjBbBEn7K0MR75L0VUkrKsefL+nLkn4qabukGyS9uOZ9F0n6bWUtqKRrf1PSTBFX0LXB5oRhtjizEbGmstrqU8CFlUlo1wL/FBEvjIhXUF4BtHaNofMpT9g8p8m1t1Ce42LWV5wwzDp3K/Ai4DRgLiIOTICKiDsj4lYASS8Eng18hHLiaCgivgU8kWnEZovghGHWgcry3G+mPGP5ZUCzxQzPo7xu1a2UZ9wWeXVTW4KcMMwWZ0TSDmCK8tpIV6R4z/nAlyPiacqLNv5OhvGZdd3yvAMwK6jZiFhTe0DSLuCdjU6WtBo4nvK6PQAHAfdSXs/HrBB8h2HWPTcDB0vaUD0g6URJ/57y3cWlEXFs5c9KYKWkY/IK1qxdThhmXVLZT+DtwBsrw2p3AZdR3o3tPMojqGpdS4O9rCXdCnwVeENlZdF12UZulo5XqzUzs1R8h2FmZqk4YZiZWSpOGGZmlooThpmZpeKEYWZmqThhmJlZKk4YZmaWyv8HzK4W0Ia2dTsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(PCA_components[0], PCA_components[1])\n",
    "plt.xlabel('PCA 1')\n",
    "plt.ylabel('PCA 2')\n",
    "plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aZGZWC4uXr98S9HM29Q+WXJil0soJ/LtFxEVAf0TcGBEfAcba2wd4GHg4Im5Lj39I9kWwjYhYEhHdEdE9bVrh9KVmZs2m2FW7tUjOllNO4O9PPx+V9A5Jc4GpY60wIv6H7Grg3JUKfwrcNdb9mZk1k2JX7dYiOVtOOYH/8+mE7CeBs8imYZ45zno/BlwqaQ3ZlcD/MM79mZk1hYXzZtPZsW1uzs4O1eyqXShvVs+P092ngMMrUWlErAa6K7EvM7OmMzw1Z6FUnVVUTj7+V0haIenO9PggSedUv2lmZq1n8fL19A9tG+n7h6LhTu5eSLb0Yj9ARKwBjqtmo8zMWlWznNydHBG/GlY2UI3GmJm1umY5ufu4pH1Jo1CSjgUerWqrzMxa1MJ5s9l+4raht5YpmaG8XD2nAUuAV0rqA34HnFTVVpmZtbDODpFytLHr5E4++84DGitlQ0TcDxwpaQdgQkQ8Xf1mmZm1nuHpGgCe7x+qeTuKBn5JHypSDkBEfLdKbTIza0ml0jU0So//dUXKjyFbhtGB38xsFBphRg+UCPwR8bHcfWXd/BOBTwO3Al+oftPMzFrLnlO66CsQ5Gs5owdGmNUjaaKkPwfuBo4Ejo2ID6S5/GZmNgqNkK4BSo/xnwacDqwAjoqIB2rVKDOzVrTywSfoHxyWn6HG6RqgdI//G8DOwBuAZZLWpNvalFzNzMzK1NPbx6W3PvSi8lqna4DSJ3dn1awVZmYtbvHy9UU79410cvfBWjbEzKyVlQruDXVy18zMKmPK5M6iz9X65K4Dv5lZDWweduFWTlfnhJpevAVlBn5JUyWNeblFM7N21tPbx3NFUjPUI2VD0cAvaYakyyVtAG4DfiXpsVQ2s1YNNDNrdqVm7dR6fB9K9/ivAK4G/igi9ouIlwN7AD3A5bVonJlZKyh1YrfW4/tQOvDvHhFXRMSWgamIGIyIy4Hdqt80M7PWUKxXP6Wrs+bj+1A68K+SdIGkgyXtmW4HS7oA6K1VA83Mmt3CebOZ1PnixVfOPeaAurSn1AVcHwJOBT5Hlo0ToA9YBlw03ooldQArgb6IOHq8+zMza1Tz507nyede4HP/cRcA06d0sXDe7Lr09qH0BVwvAN9Kt2o4nSz5285V2r+ZWUPo6e3jght+C8DUHbara9CH0rN6dh/2+CRJX5e0QLnVWMZI0l7AO4B/Hc9+zMwaXW7VrQ1PbwbgiWdfYNHStfT09tWtTaXG+K/J3ZF0DvBBYBXwVuDL46z3q8CngKITWNMXzEpJKzds2DDO6szM6qPUqlv1Uirw5/fq3wO8JyK+A5xAlpt/TCQdDTwWEatKbRcRSyKiOyK6p02bNtbqzMzqqlFW3cpX6uRul6S5ZF8OHRHxLEBE9EsqfO1xeQ4DjpH0dmASsLOk70XESePYp5lZQ5oyuZMnn+t/UXk9LtzKKRX4H2XrkM4TkvaIiEcl7QYMjLXCiFgELAKQ9BbgLAd9M2tFPb19PFUg6AMc/sr6jWSUmtVzeJGnNgJvqk5zzMxax+Ll64ueyLz+N/U7d1lukradcz/T1bvPVaLyiLjBc/jNrFWVGsev5xh/uWmZbxj208zMRlAqB389x/hHm49/XPP3zczaSbEc/FCf5Gw5XojFzKwKTrzwlqI5+IHGvHLXzMzG5sQLb+Hm3z5R9PnpdRzmgdEH/mKLxJuZGXBOz9qSQR/qO8wD5Qd+DftpZmbD9PT28b1bHyq5Tb1y8OcrN/B/YNhPMzMbppz8O/XKwZ9vxMAv6bURcQ9A7mfKt2NmZnn6Rpibf9i+U+ve24fyevwXSjow90DS8cD/r16TzMyaz0hpljsnwKV/cWiNWlNaqVw9OccCP5R0AvBGspW53lbVVpmZNZGe3j4+8YPVJbdZ/L45NWrNyEYM/BFxv6TjgB7gIeBtEVG/a43NzBrMp69aw1CJOY+NcEI3X9HAL2kt207fnAp0ALdJIiIOqnbjzMwa3Vu/fAObB4pfqAWNcUI3X6kev0/gmpkVcU7P2hGnbkLj9fahdFrmB2vZEDOzZvHWL9/AvY89W9a2jdbbh/JO7pqZGeX38nNOOmRGw/X2wbl6zMzKMtqgf9i+U/n8/FdVsUVj58BvZlaGy24rP+jv95IdGmbOfiEO/GZmI+jp7Ss5XTPfYftO5dpPvKWq7Rkvj/GbmY3g01etGXGbiRPEP73v1Q05pj+cA7+ZWRHljusftu/Uhh7aGa7mgV/S3sB3gZeSXSC2JCK+Vut2mJkVM5oTuV/9wJym6OXnq0ePfwD4ZETcLmknYJWkayPirjq0xcxsGyOtnpWvES/OKkfNT+5GxKMRcXu6/zRwN9B8vzkzazmjCfrQmBdnlaOus3okzQTmArfVsx1mZqMN+o2SW38s6hb4Je0IXAWcERF/KPD8AkkrJa3csGFD7RtoZm2jp7dv1EG/mU7mDleXWT2SOsmC/qURsbTQNhGxBFgC0N3d7UXezawqenr7OPOK0rn0c5ppymYp9ZjVI+Ai4O6I+HKt6zczg7Hl3WnUFAyjVY8e/2HAB4G1knJfs38TET+pQ1vMrM2MJedOMw/rFFLzwB8RvwBU63rNzEZ7AreR1smtJF+5a2Ytrae3j3OXrWPjpv5Rv7aR1smtJAd+M2tZo+3h52vUXPqV4MBvZi1pNKtkDddKJ3ILceA3s5Yy2pO3+XbYroMvvPtVLdvTz3HgN7OmNZ4gn68ZE62NhwO/mTW88ZygLUXAV9os6IMDv5k1oEr15Etpl2GdQhz4zazuenr7WLR0DZv6h6paT6ukXBgvB34zq5lzetZy6a0PUY/kW614Be5YOfCb2bjVqsc+Fu7lv5gDv5mVpZGDeyETBCcc3Nrz8cfKgd/MSqrFidbxaucTtWPhwG/W5pqtJ+8gP34O/GZtphl68DkerqkOB36zFtRsvfh87tFXnwO/WZNopp76SASc2OKJ0BqZA79ZnVUrHUE9OKA3Bwd+sxpq5iGYfLtO7uSz7zzAwzFNyoHfrIJaJbAX4hOtrcOB34zWGj8fD59YbQ91CfySjgK+BnQA/xoR59WjHTZ2DpTNz0G+fdU88EvqAP4ZeCvwMPBrScsi4q5K1tPK/3KbjYYDvA1Xjx7/64H7IuJ+AEmXA+8CKhb4e3r7+MQVq3HIt1bksXYbr3oE/unAf+c9fhg4uJIVLF6+3kHfmpJ751YLDXtyV9ICYAHAjBkzRvXaRzZuqkaTzCrKQd7qpR6Bvw/YO+/xXqlsGxGxBFgC0N3dPap1G/ac0kWfg7/Vmee6W6OqR+D/NbCfpFlkAf844IRKVrBw3myP8duYuBdu7aDmgT8iBiT9NbCcbDrnxRGxrpJ15P5oPaun+hwozZqPIuqx+uXodHd3x8qVK+vdDDOzpiJpVUR0Dy+fUI/GmJlZ/Tjwm5m1GQd+M7M248BvZtZmHPjNzNpMU8zqkbQBeHCML98deLyCzWlUPs7W4uNsHfU8xpdFxLThhU0R+MdD0spC05lajY+ztfg4W0cjHqOHeszM2owDv5lZm2mHwL+k3g2oER9na/Fxto6GO8aWH+M3M7NttUOP38zM8rR04Jd0lKT1ku6TdHa92zMeki6W9JikO/PKpkq6VtK96eeuqVySvp6Oe42k19Sv5eWTtLek6yXdJWmdpNNTeasd5yRJv5J0RzrOz6XyWZJuS8dzhaTtUvn26fF96fmZ9Wz/aEnqkNQr6cfpccsdp6QHJK2VtFrSylTWsJ/blg38eYu6/xmwP3C8pP3r26pxuQQ4aljZ2cCKiNgPWJEeQ3bM+6XbAuBbNWrjeA0An4yI/YFDgNPSe9Zqx7kZOCIiXg3MAY6SdAjwReArEfFy4Eng1LT9qcCTqfwrabtmcjpwd97jVj3OwyNiTt7Uzcb93EZES96AQ4HleY8XAYvq3a5xHtNM4M68x+uBPdL9PYD16f6/AMcX2q6ZbsCPgLe28nECk4HbydadfhyYmMq3fH7J1q44NN2fmLZTvdte5vHtRRb0jgB+DKhFj/MBYPdhZQ37uW3ZHj+FF3VvtdVCXhoRj6b7/wO8NN1v+mNP/+bPBW6jBY8zDX+sBh4DrgV+C2yMiIG0Sf6xbDnO9PxTwG61bfGYfRX4FGxZEG83WvM4A7hG0qq0Xjg08Oe2YRdbt9GJiJDUElO0JO0IXAWcERF/kLTluVY5zogYBOZImgJcDbyyzk2qOElHA49FxCpJb6l3e6rsDRHRJ+klwLWSfpP/ZKN9blu5x1/Wou5N7veS9gBIPx9L5U177JI6yYL+pRGxNBW33HHmRMRG4HqyIY8pknKdsfxj2XKc6fldgP+tcVPH4jDgGEkPAJeTDfd8jdY7TiKiL/18jOyL/PU08Oe2lQP/lkXd06yB44BldW5TpS0DTk73TyYbE8+VfyjNHjgEeCrvX86GpaxrfxFwd0R8Oe+pVjvOaamnj6QusvMYd5N9ARybNht+nLnjPxa4LtLgcCOLiEURsVdEzCT7+7suIk6kxY5T0g6SdsrdB94G3Ekjf27rfVKkyidc3g7cQzZ++pl6t2ecx/J94FGgn2xM8FSy8c8VwL3Az4CpaVuRzWj6LbAW6K53+8s8xjeQjZWuAVan29tb8DgPAnrTcd4J/G0q3wf4FXAfcCWwfSqflB7fl57fp97HMIZjfgvw41Y8znQ8d6TbulysaeTPra/cNTNrM6081GNmZgU48JuZtRkHfjOzNuPAb2bWZhz4zczajAN/FUgaTFn67pR0paTJJbY9RiNkDpU0U9IJlW9p0frmjyWhnaRnqtGesZJ0iqRvpvsflfShCu33jFLvaZHXPCBp9zHUda6kvvR5Wi3p7XnPLUoZHtdLmjfafdeKpCmS/qqC+xvxvcx/7+3FHPirY1NkWfoOBF4APlpsw4hYFhHnjbC/mUBNAn+6YnI+WUbTppIyshYUEd+OiO9WqKozyJKr1cpX0udpTkT8BCB9MR8HHECWtfWCUsdfSXlX3ZZrClCxwF/h93JUxnDsDcmBv/p+Drw85ebuSfm3b5V0ELyoV3pJytP9S0n3S8pd3Xge8MbU4ztT0gHK8rmvTvvbL7/ClADskvQfx1pJZ6byOanuNZKu1tb84DdI+qqyPOKfBo4BFqf975tuP00JqH4u6ZXpdbMk3ZLq+HyxX0A67lXKcs8vyCs/StLtyvLSr0hlO0r6t7TPNZLem8qPT2V3Svpi3j6ekfQlSXcAh0r6sKR7JP2KLGVAbrtzJZ2Vd7xfTL/DeyS9MZVPlvQDZesBXK0sJ3wuxW5uPx8H9gSul3R9qbYV+V10SfovSX9RarsyvAu4PCI2R8TvyC56en2B+p6R9IX0O75V0ktT+TRJV0n6dbodlspfn97T3vQ5nJ3KT5G0TNJ1wAplV6tenH6HvZLelbYr9Nk8D9g3lS0e1r6Zku6WdGH6fFyj7GpmSnzu8t/L16V6VktarLz1KoA90+vvlXR+Xp1FP0t594+VdEm6f4mkb0u6DTifVlDvq95a8QY8k35OJLtM+y+BbwCfTeVHAKvT/VOAb6b7l5BduTiBrMd9Xyp/C+mqx/T4G8CJ6f52QNew+l8LXJv3eEr6uQZ4c7r/d8BX0/0bgAvytr8EODbv8Qpgv3T/YLJL6SFdep7un5Y77gK/j9wVi11kV6ruBkwjy1A4a9g2X8y1Kz3elSzQPpReMxG4Dpifng/g/en+HnnbbQfcnPe7PRc4K+94v5Tuvx34Wbp/FvAv6f6BZOsDvOiqSvJS8JZqW4HXzCS7gvNDeeU/Z+tVyvm3I/Pa/UB67y4Gdk3l3wROytvPRfnvWV55AO9M988Hzkn3LyNLLAYwgyxNBsDObE2ZfCRwVd7n9OG89+kfcvWT9ejvAXagwGeTYenEh7VvZvo9z0mPf5C332Kfu/z38k62pnI+L1dPau/9ZPl+JgEPkuXHKfVZeiavXccCl+T9PfwY6Kh3bKnUrSX+bWlAXcpS7kL2h30RWXrh9wJExHWSdpO0c4HX9kTEEHBXrndWwC3AZyTtBSyNiHuHPX8/sI+kbwD/SZYudheyL4Ab0zbfIfuSybmiUEXKMmX+CXCltmbJ3D79PCx3TMC/U3zhjI9Lene6vzfZAhTTgJsi660SEU+k548kG/e0CIkAAAQSSURBVMIglT8p6U3ADRGxIbXpUuBNQA8wSJbUDbLgkL/dFcArirQplwBuFVnwgSxlxNdSvXdKWlPktfleV6Jtw/0IOD8iLs07vjeOsP9vAX9PFsD/HvgS8JEy2pXzAlnQguxY35ruHwnsn/ee7pze612A76SeegCdefu6Nu99ehtZAraz0uNJZF8gL/ps5tVRzO8iIvf3sgqYOcLnDsjOHQA7RcQtqegy4Oi8TVZExFNp27uAl5F1Osp9v/JdGVlG1ZbgwF8dmyJiTn5BGR/+nM35Lyu0QURclv7tfAfwE0n/LyKuy3v+SUmvBuaRnV94P3DmCPU+W6R8Aln+9DlFni+Z80NZOt4jyXplz0m6gSxIVMrzY/yDzP2eB6nd38HNZKttXRapKynp58BOBbY9KyJ+FhG/zxVIupCtQbzcDI/9ubrY9lgnAIdExPP5Gysbdrw+It6tbE2EG/Kezv+MCHhvRKwfVt/dwz+bZB2RUvI/84Nk/yWM9Lkrx/D9jvQ+53+Wh39Gi/19NCWP8dfOz4ETYUswfDwi/lDma58mLzhI2ge4PyK+TtaLPCh/Y2WzRyZExFXAOcBrUs/nydx4NvBB4EYK21JfauPvJL0v7VvpSwWyQJbrnZ9YZF+7kC2n91waoz0kld8KvEnSrLTfqan8WrJho9yx7EqWsOvNknZXdgLz+CJtvy1tt5uy9M7vK9KmYm4m+5LMnTx9VZHt8t+PctsG8LdkSw3+c64gIt4YW0/c5t9+ltqxR97r3002tAHZMNtxytapnUX2X9SvRnGs1wAfyz2QlAuwu7D1C+SUEq9fDnxMqUcjaW76Weizuc3ntxwjfO5y22wEnpZ0cCo6jpGVer9+L+mPJU0g+123LAf+2jkXeG0aPjiPrelay7EGGFR2gu5MsuB0ZxpOOhAYPsNhOnBDev57ZMtOkupcnNowh2ycv5DLgYXppN2+ZEH9VGUnUNeRnViEbC3V0yStpfgKQj8FJkq6Ox33rQDpX+0FwNK039xQ0+eBXdOJtzvI1jF9lGy90uvJMiCuiogfDauHtN25ZMMNN7PtOq/luACYloYFPp+O9akC2y0Bfirp+nLblud0sqHAck8Snp9ORK4BDif95xYR68jGw+8i+x2fNsr/fD4OdKcTo3exdebZ+cA/SuqldA/578mGgdZIWpceQ4HPZkT8L3Bzek8XF95dQcU+d/lOBS5M9e1A4fdrixHer7PJ/qP6JVkm3Jbl7JxmSeoBdkbE8+kL72fA7Ih4oc5NsyIk7RgRz6T7Z5OtXXt6nZvV8DzGb7bVZLJpmp1kY9h/5aDf8N4haRFZLHuQ0sNTlrjHb2bWZjzGb2bWZhz4zczajAO/mVmbceA3M2szDvxmZm3Ggd/MrM38H87WSGAChcjNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DBSCAN.plot_param_est_curve(PCA_components.loc[:, [0,1]].to_numpy(), 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusterer = DBSCAN(eps=2, min_pts=50)\n",
    "clusterer.fit(PCA_components.loc[:, [0,1]].to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'PCA 2')"
      ]
     },
     "execution_count": 14,
     "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.scatter(PCA_components[0], PCA_components[1], c=clusterer.labels_)\n",
    "plt.xlabel('PCA 1')\n",
    "plt.ylabel('PCA 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dbscan_labels = pd.concat([data[all_headers[:2]], pd.Series(clusterer.labels_)], axis=1)\n",
    "df_dbscan_labels.columns = ['State', 'Year', 'Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>State</th>\n",
       "      <th>Year</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>andhra pradesh</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arunachal pradesh</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>assam</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bihar</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chhattisgarh</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>d&amp;n haveli</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>daman &amp; diu</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>lakshadweep</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>puducherry</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>527 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 State  Year  Label\n",
       "0       andhra pradesh  2001      1\n",
       "1    arunachal pradesh  2001      1\n",
       "2                assam  2001      1\n",
       "3                bihar  2001      1\n",
       "4         chhattisgarh  2001      1\n",
       "..                 ...   ...    ...\n",
       "522         d&n haveli  2015      1\n",
       "523        daman & diu  2015      1\n",
       "524              delhi  2015      1\n",
       "525        lakshadweep  2015      1\n",
       "526         puducherry  2015      1\n",
       "\n",
       "[527 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dbscan_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>murder</th>\n",
       "      <th>rape</th>\n",
       "      <th>foeticide</th>\n",
       "      <th>kidnapping and abduction</th>\n",
       "      <th>abetment of suicide</th>\n",
       "      <th>exposure and abandonment</th>\n",
       "      <th>procuration of minor girls</th>\n",
       "      <th>selling girls for prostitution</th>\n",
       "      <th>prohibition of child marriage act</th>\n",
       "      <th>other crimes</th>\n",
       "      <th>total</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>west bengal</td>\n",
       "      <td>2006</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>madhya pradesh</td>\n",
       "      <td>2009</td>\n",
       "      <td>127.0</td>\n",
       "      <td>1071.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>4646.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>madhya pradesh</td>\n",
       "      <td>2010</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1182.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3004.0</td>\n",
       "      <td>4912.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>west bengal</td>\n",
       "      <td>2010</td>\n",
       "      <td>16.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>madhya pradesh</td>\n",
       "      <td>2011</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2312.0</td>\n",
       "      <td>4381.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>west bengal</td>\n",
       "      <td>2011</td>\n",
       "      <td>46.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>madhya pradesh</td>\n",
       "      <td>2012</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1632.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>5168.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>maharashtra</td>\n",
       "      <td>2012</td>\n",
       "      <td>212.0</td>\n",
       "      <td>917.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>893.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>3456.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>uttar pradesh</td>\n",
       "      <td>2012</td>\n",
       "      <td>463.0</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4239.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>6033.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>west bengal</td>\n",
       "      <td>2012</td>\n",
       "      <td>46.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1706.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>chhattisgarh</td>\n",
       "      <td>2013</td>\n",
       "      <td>49.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1282.0</td>\n",
       "      <td>3737.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>madhya pradesh</td>\n",
       "      <td>2013</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2262.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3502.0</td>\n",
       "      <td>8247.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>maharashtra</td>\n",
       "      <td>2013</td>\n",
       "      <td>191.0</td>\n",
       "      <td>1546.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2595.0</td>\n",
       "      <td>6410.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>uttar pradesh</td>\n",
       "      <td>2013</td>\n",
       "      <td>482.0</td>\n",
       "      <td>1381.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6002.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1921.0</td>\n",
       "      <td>9857.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>west bengal</td>\n",
       "      <td>2013</td>\n",
       "      <td>68.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1388.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2530.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>madhya pradesh</td>\n",
       "      <td>2014</td>\n",
       "      <td>131.0</td>\n",
       "      <td>2352.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6339.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3282.0</td>\n",
       "      <td>15085.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>maharashtra</td>\n",
       "      <td>2014</td>\n",
       "      <td>195.0</td>\n",
       "      <td>1714.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2616.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>8115.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>uttar pradesh</td>\n",
       "      <td>2014</td>\n",
       "      <td>543.0</td>\n",
       "      <td>1538.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5875.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>14835.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>west bengal</td>\n",
       "      <td>2014</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2351.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>852.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>4909.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>assam</td>\n",
       "      <td>2015</td>\n",
       "      <td>16.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1303.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>2835.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>madhya pradesh</td>\n",
       "      <td>2015</td>\n",
       "      <td>124.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5265.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2542.0</td>\n",
       "      <td>12859.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>maharashtra</td>\n",
       "      <td>2015</td>\n",
       "      <td>207.0</td>\n",
       "      <td>2231.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6960.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>13921.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>tamil nadu</td>\n",
       "      <td>2015</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>2617.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>west bengal</td>\n",
       "      <td>2015</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>4963.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              state  year  murder    rape  foeticide  \\\n",
       "202     west bengal  2006     3.0    20.0        0.0   \n",
       "293  madhya pradesh  2009   127.0  1071.0       39.0   \n",
       "328  madhya pradesh  2010   144.0  1182.0       18.0   \n",
       "342     west bengal  2010    16.0    73.0        0.0   \n",
       "363  madhya pradesh  2011   123.0  1262.0       38.0   \n",
       "377     west bengal  2011    46.0   252.0        0.0   \n",
       "398  madhya pradesh  2012   140.0  1632.0       64.0   \n",
       "399     maharashtra  2012   212.0   917.0       22.0   \n",
       "410   uttar pradesh  2012   463.0  1040.0       11.0   \n",
       "412     west bengal  2012    46.0   285.0        0.0   \n",
       "424    chhattisgarh  2013    49.0   595.0       15.0   \n",
       "433  madhya pradesh  2013   114.0  2112.0       79.0   \n",
       "434     maharashtra  2013   191.0  1546.0       17.0   \n",
       "445   uttar pradesh  2013   482.0  1381.0       17.0   \n",
       "447     west bengal  2013    68.0   377.0        3.0   \n",
       "468  madhya pradesh  2014   131.0  2352.0       30.0   \n",
       "469     maharashtra  2014   195.0  1714.0        7.0   \n",
       "481   uttar pradesh  2014   543.0  1538.0       11.0   \n",
       "483     west bengal  2014    64.0     0.0        0.0   \n",
       "493           assam  2015    16.0    43.0        0.0   \n",
       "504  madhya pradesh  2015   124.0  1568.0       17.0   \n",
       "505     maharashtra  2015   207.0  2231.0       11.0   \n",
       "514      tamil nadu  2015    87.0     0.0        0.0   \n",
       "519     west bengal  2015    53.0     0.0        0.0   \n",
       "\n",
       "     kidnapping and abduction  abetment of suicide  exposure and abandonment  \\\n",
       "202                     156.0                  0.0                       3.0   \n",
       "293                     427.0                  8.0                     103.0   \n",
       "328                     440.0                  7.0                      93.0   \n",
       "342                     332.0                  3.0                       0.0   \n",
       "363                     517.0                 12.0                      89.0   \n",
       "377                     660.0                  3.0                       1.0   \n",
       "398                     630.0                 19.0                     134.0   \n",
       "399                     893.0                 13.0                     199.0   \n",
       "410                    4239.0                 57.0                       2.0   \n",
       "412                     767.0                  7.0                       8.0   \n",
       "424                    1634.0                 95.0                       7.0   \n",
       "433                    2262.0                 16.0                     124.0   \n",
       "434                    1698.0                 26.0                     259.0   \n",
       "445                    6002.0                 36.0                       3.0   \n",
       "447                    1388.0                  5.0                       1.0   \n",
       "468                    6339.0                  6.0                     148.0   \n",
       "469                    2616.0                 19.0                     225.0   \n",
       "481                    5875.0                  1.0                       1.0   \n",
       "483                    2351.0                  3.0                       0.0   \n",
       "493                     383.0                  0.0                       0.0   \n",
       "504                    5265.0                  8.0                     145.0   \n",
       "505                    6960.0                 13.0                     219.0   \n",
       "514                     398.0                  1.0                       2.0   \n",
       "519                    1951.0                  4.0                       0.0   \n",
       "\n",
       "     procuration of minor girls  selling girls for prostitution  \\\n",
       "202                        77.0                           114.0   \n",
       "293                         1.0                             1.0   \n",
       "328                        18.0                             2.0   \n",
       "342                       200.0                           115.0   \n",
       "363                        20.0                             3.0   \n",
       "377                       298.0                            87.0   \n",
       "398                        21.0                             5.0   \n",
       "399                        31.0                             2.0   \n",
       "410                         2.0                            18.0   \n",
       "412                       369.0                            56.0   \n",
       "424                        44.0                             0.0   \n",
       "433                        21.0                             3.0   \n",
       "434                        41.0                            13.0   \n",
       "445                         0.0                             0.0   \n",
       "447                       486.0                            69.0   \n",
       "468                         5.0                             1.0   \n",
       "469                         7.0                             4.0   \n",
       "481                         0.0                             0.0   \n",
       "483                       852.0                            67.0   \n",
       "493                      1303.0                             0.0   \n",
       "504                         5.0                             5.0   \n",
       "505                         6.0                             1.0   \n",
       "514                         9.0                             0.0   \n",
       "519                      1003.0                            91.0   \n",
       "\n",
       "     prohibition of child marriage act  other crimes    total  Label  \n",
       "202                                6.0          49.0    432.0     -1  \n",
       "293                                0.0        2869.0   4646.0     -1  \n",
       "328                                4.0        3004.0   4912.0     -1  \n",
       "342                                0.0          93.0    880.0     -1  \n",
       "363                                5.0        2312.0   4381.0     -1  \n",
       "377                               25.0          78.0   1450.0     -1  \n",
       "398                                3.0        2520.0   5168.0     -1  \n",
       "399                                6.0        1157.0   3456.0     -1  \n",
       "410                                5.0         196.0   6033.0     -1  \n",
       "412                               43.0         122.0   1706.0     -1  \n",
       "424                                1.0        1282.0   3737.0     -1  \n",
       "433                                6.0        3502.0   8247.0     -1  \n",
       "434                               16.0        2595.0   6410.0     -1  \n",
       "445                                5.0        1921.0   9857.0     -1  \n",
       "447                               43.0          89.0   2530.0     -1  \n",
       "468                               15.0        3282.0  15085.0     -1  \n",
       "469                               14.0         877.0   8115.0     -1  \n",
       "481                               10.0         256.0  14835.0     -1  \n",
       "483                               37.0         161.0   4909.0     -1  \n",
       "493                               14.0         131.0   2835.0     -1  \n",
       "504                                9.0        2542.0  12859.0     -1  \n",
       "505                               24.0        1120.0  13921.0     -1  \n",
       "514                               77.0         396.0   2617.0     -1  \n",
       "519                               40.0         237.0   4963.0     -1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_pickle('./state_clubbed_df.pickle')\n",
    "concat_df = pd.concat([df, df_dbscan_labels['Label']], axis=1)\n",
    "outliers = concat_df[concat_df['Label'] == -1]\n",
    "outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers.to_pickle('dbscan-outliers.pickle')"
   ]
  }
 ],
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