pbl1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import scale\n",
    "from numpy import random, float, array\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.cm as cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>State</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>Rape</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>58.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>236.0</td>\n",
       "      <td>21.2</td>\n",
       "      <td>270.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>44.5</td>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>294.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>333.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>190.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>218.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>California</td>\n",
       "      <td>91.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>40.6</td>\n",
       "      <td>325.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       State  UrbanPop  Murder  Assault  Rape  Total\n",
       "0           0     Alabama      58.0    13.2    236.0  21.2  270.4\n",
       "1           1      Alaska      48.0    10.0    263.0  44.5  317.5\n",
       "2           2     Arizona      80.0     8.1    294.0  31.0  333.1\n",
       "3           3    Arkansas      50.0     8.8    190.0  19.5  218.3\n",
       "4           4  California      91.0     9.0    276.0  40.6  325.6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:/Users/Viraj/Documents/crime_data_2.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the matplotlib figure\n",
    "\n",
    "f, ax = plt.subplots(figsize=(16, 10))\n",
    "\n",
    "# Load the dataset\n",
    "stats = df.sort_values(\"Total\", ascending=False)\n",
    "\n",
    "# Plot the total. x= and y= are actual column names\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.barplot(x=\"Total\", y=\"State\", data=stats,\n",
    "            label=\"Total arrests\", color=\"b\")\n",
    "\n",
    "# Plot the population\n",
    "sns.barplot(x=\"UrbanPop\", y=\"State\", data=stats,\n",
    "            label=\"Urban population in 4\", color=\"r\")\n",
    "\n",
    "# Add a legend and informative axis label\n",
    "ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n",
    "ax.set(xlim=(0, 400), ylabel=\"State\",\n",
    "       xlabel=\"Total arrests vs population in 4\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(16, 10))\n",
    "\n",
    "stats = df.sort_values(\"Total\", ascending=False)\n",
    "\n",
    "sns.set_color_codes(\"pastel\")\n",
    "\n",
    "sns.barplot(x=\"Total\", y=\"State\", data=stats,\n",
    "            label=\"Total\", color=\"g\")\n",
    "\n",
    "sns.barplot(x=\"Assault\", y=\"State\", data=stats,\n",
    "            label=\"Assault\", color=\"b\")\n",
    "\n",
    "sns.barplot(x=\"Rape\", y=\"State\", data=stats,\n",
    "            label=\"Rape\", color=\"y\")\n",
    "\n",
    "sns.barplot(x=\"Murder\", y=\"State\", data=stats,\n",
    "            label=\"Murder\", color=\"r\")\n",
    "\n",
    "# Add a legend and informative axis label\n",
    "ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n",
    "ax.set(xlim=(0, 400), ylabel=\"State\",\n",
    "       xlabel=\"No of arrests for each crime\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X = df[['Murder', 'Assault', 'Rape', 'UrbanPop']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_errors = []\n",
    "cluster_range = range( 1, 10 )\n",
    "cluster_errors = []\n",
    "\n",
    "for num_clusters in cluster_range:\n",
    "  clusters = KMeans( num_clusters )\n",
    "  clusters.fit( X_scaled )\n",
    "  cluster_errors.append( clusters.inertia_ )\n",
    "clusters_df=pd.DataFrame({\"num_cluster\": cluster_range,\"cluster_errors\": cluster_errors })\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>num_cluster</th>\n",
       "      <th>cluster_errors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20.993688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>12.300723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8.944545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.749051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_cluster  cluster_errors\n",
       "0            1       44.000000\n",
       "1            2       20.993688\n",
       "2            3       12.300723\n",
       "3            4        8.944545\n",
       "4            5        5.749051"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_df[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "plt.plot( clusters_df.num_cluster, clusters_df.cluster_errors, marker = \"o\" );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>Rape</th>\n",
       "      <th>UrbanPop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.2</td>\n",
       "      <td>236.0</td>\n",
       "      <td>21.2</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>44.5</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>294.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.8</td>\n",
       "      <td>190.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>40.6</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Murder  Assault  Rape  UrbanPop\n",
       "0    13.2    236.0  21.2      58.0\n",
       "1    10.0    263.0  44.5      48.0\n",
       "2     8.1    294.0  31.0      80.0\n",
       "3     8.8    190.0  19.5      50.0\n",
       "4     9.0    276.0  40.6      91.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['Murder', 'Assault', 'Rape', 'UrbanPop']]\n",
    "\n",
    "clusters = KMeans(4)  # 4 clusters!\n",
    "clusters.fit( X )\n",
    "clusters.cluster_centers_\n",
    "clusters.labels_\n",
    "\n",
    "df['Crime_clusters'] = clusters.labels_\n",
    "df.head()\n",
    "df.sort_values(by=['Crime_clusters'],ascending = True)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('UrbanPop' ,'Total', data=df,\n",
    "        hue = \"Crime_clusters\",\n",
    "        fit_reg=False, height = 6 );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x0dr2kJlfYfT75gAvH2X7BC4aQz2SpDKtA35zP+t+fID9nhgx/+SI5Sd5KueOAF4yIjQPJBilj9cIu9nzVvasEfO/DiwDzgbeERFLRtl/1FrqoP/J75mZH4uI2+tjfjEifj8zv3Sw4vd7jz0izqovAyysHxwz/Lq2/qUkSZpMXwKeFhH/ZbghIn4R+OVJOPbNwJtGHHfpOLbd+1L8w8CCiHhmRDwNeGW93RHAosy8DfgTYC5wJLANmDNi/zHVEhGLgY2Z+bdUt6t//iC/I3DgznMPUd3r2FlPh18rgV8by8ElSRqr+srtq4Ez6q+7rQMuZ5SO1IfgzUBv3QntXqoObfvzF8C8iLgnIr7NU5fzh+scBK6g+gr354AN9aoO4J8j4m7gW1T30bcC/wd49XDnuXHU8lvAPfXt7BOB68fyi0b1Ph5gg4gZmTklz9B7e3tzzZo1jf6MVRv6uWr1RjZt2c6iebO5cNlilp+44OA7SpL2Z3+3aTUJ9nuPvf6LI+v5fdZn5pguCUxnqzb0c9nKdXR2BHO7OunftpPLVq7jCjDcJUlT0oE6z72yZVVMUVet3khnRzB7ZvU2zZ45g+27dnPV6o0GuyRNcxFxHnDxXs3/LzOndUfu/QZ7Zn6/lYVMRZu2bGduV+cebV2dHfRt2d6miiRJkyUzPwJ8pN11TLaDPnkuIrZFxGP1a2dEDEXEgR4NW4xF82azY3Boj7Ydg0P0zJvdpookSTqwgwZ7Zs7JzKPq1yzgNcDfN19a+124bDGDQ8n2XbvJrKaDQ8mFyxa3uzRJkkY17mfFZ+a/AKc1UMuUs/zEBVxx9hIWzJnFozsGWTBnFlecvcT765KkKWssj5T9TyMWjwB6OfATeYqy/MQFBrkkaUwi4kyq59J3UD2z/t2trmEsj5T9jRHzu4EHgBWNVCNJUosce8m/ngn8MXAccD/wVw+8+9e/cKjHq4eUfT9wBtWIpXdExMrMvHcy6h2rgwZ7ZjoYiySpKHWov5/qufKPAMcA7z/2kn+9aALhfjJwX2ZuBKhHK10BTK1gj4hZwPnAEkY86D4z39hgXZIkNemPqUJ9+PvL20e0H2qwLwQ2jVjuoxqGvKXG0nnun4Cfono+/L8BPVQPtJckabo6jqfCfNj2uv1Qjfao3Jb3SRtLsP9sZr4D+HFmXkc1fNzzmy1LkqRG3Q/s/VCS2XX7oeoDFo1Y7mFyBrAZl7EE+2A93RoRzwOeARzbWEWSJDXvr4Cn8VS4z66X/2oCx7wDOD4ijouImcDrqEZEbamxBPvV9Vi0/4OqwHuB/9VoVZIkNajuIHcRsBmYX08n0nGOeiTUNwFfBNYDN2bmukkod1wOOmzrVNaKYVslSZPOYVsbNJZnxV8cEUdF5UMRcWdE/GoripMkSeMzlkvxb8zMx4BfBRYA5wEtf5KOJEk6uLEE+/Alk1cAH8nMb+NlFEmSpqSxBPs3I+JmqmD/YkTMAZ5stixJknQoxvKs+POBpcDGzNweEfOpLsdLkqQpZixn7C8BvpOZWyPid6m+9vZos2VJkqRDMZZg/yCwPSJeAPwJ8H3g+karkiRpGoqID0dEf0Tc064axnIpfndmZkSsAK7MzGsi4pymC5MkqVGXP2OfYVu5/NFDfkBN7Vrg72njCfBYzti3RcTbgTcA/1qPNzuWPwgkSZqaqlB/P9VwrT8ZtrVuP2SZubo+XtuMJdh/i2pou/My84fAqcDTG61KkqRmjTZs6xN1+7R20DPvzPxhRHwJ+O2I+GeqyxV/03hlkiQ15zj2PbOe6LCtU8J+gz0iTqAameb1wH8An6B6tvyvtKg2SZKacj/V5feRY7JPdNjWKeFAl+I3AC8HfiMzfykz/w4Yak1ZkiQ1qolhW6eEAwX7a4AfArdFxD9GxMvxUbKSpBJUvd/3GbZ1or3iI+LjwNeAn4uIvog4f8K1jreGgw3bGhFPB15FdUn+NOA64DOZeXPz5R2Yw7ZK0rTkSWKDDtorPjN/nJkfzcxXAj3AWuCSxiuTJEnjNpavu/1EZj6SmVdl5mlNFSRJkg7duIJdkiRNbQa7JEkFMdglSSqIwS5JUkEMdkmSJklELIqI2yJifUSsi4iLW12Do7RJkg5Lz7/u+fsM23r3OXdPdNjW3cBbM/POiJgDfDMibsnMeyd43DFr7Ix9tMHmI+LyiHgwItbWr1eMWPf2iLgvIr4TEb/WVF2SJNWhvs+wrXX7IcvMzZl5Zz2/DVgPLJxguePS5KX4a4HR3qD3ZebS+nUTQEScRDXgzJJ6nw/U475LktSExodtjYhjgRcCt0/WMceisWAf52DzK4AbMvOJzLwfuA84uanaJEmHvePYc2Q3mMRhWyPiSOBTwFsy87HJOOZYtaPz3Jsi4q76Uv28um0hsGnENn20+NKFJOmwcj9Pjew2bFKGbY2ITqpQ/2hmfnqixxuvVgf7B4HnAEupRtL567p9tAEBRh2dJiIuiIg1EbFmYGCgmSolSaVrZNjWiAjgGmB9Zr53QhUeopYGe2Y+nJlDmfkk8I88dbm9D1g0YtMe4KH9HOPqzOzNzN7u7u5mC5YkFanu/b7PsK2T0Cv+VOANwGmjdRRvhZZ+3S0ijsnMzfXiq4HhHvMrgY9FxHuBnwaOB77RytokSYeXOsQnGuR7yMyv0OZhaRsL9nqw+eXAsyKiD/gzYHlELKW6zP4AcCFAZq6LiBuBe6m+A3hRZg41VZskSaWKzFFvZU8Lvb29uWbNmnaXIUkan7ae0ZbOR8pKklQQg12SpIIY7JIkFcRglySpIAa7JEmTJCJmRcQ3IuLb9bCt72x1DQ7bKkk6LK0/8bn7DNv63A3rJ/q99ieA0zLz8frRsl+JiM9n5tcneNwx84xdknTYqUN9n2Fb6/ZDlpXH68XO+tXS75Ub7JKkw1Fjw7ZGREdErAX6gVsys4xhWyVJmsIaG7a1HhNlKdW4JydHxPMmeszxMNglSYejxoZtHZaZW4FVwIQu74+XwS5JOhw1NWxrd0TMree7gNOBDRM55ngZ7JKkw07d+32fYVsnoVf8McBtEXEXcAfVPfbPTfCY4+IgMJKkVnMQmAZ5xi5JUkEMdkmSCmKwS5JUEINdkqSCGOySJBXEYJckqSAGuyRJBTHYJUkqiOOx7+27t8BXr4St34e5PwMvvRhOOKPdVUmSNCaesY/03Vvg82+DbQ/DrHnV9PNvq9olSZoGDPaRvnolHDETZs6GiGp6xMyqXZKkacBgH2nr96Gza8+2zi7Y+oP21CNJ0jgZ7CPN/RkY3LFn2+AOmPvs9tQjSdI4GewjvfRieHIX7NoOmdX0yV1VuyRJ04DBPtIJZ8BZ74E5R8POrdX0rPfYK16SNG34dbe9nXCGQS5JmrY8Y5ckqSAGuyRJBTHYJUkqiMEuSVJBDHZJkgpisEuSVBCDXZKkghjskiQVxGCXJKkgBrskSQUx2CVJKojBLklSQRoL9oj4cET0R8Q9I9rmR8QtEfG9ejqvbo+I+NuIuC8i7oqIFzVVlyRJJWvyjP1a4My92i4Bbs3M44Fb62WAs4Dj69cFwAcbrEuSpGI1FuyZuRp4ZK/mFcB19fx1wKtGtF+fla8DcyPimKZqkySpVK2+x350Zm4GqKcL6vaFwKYR2/XVbfuIiAsiYk1ErBkYGGi0WEmSppup0nkuRmnL0TbMzKszszcze7u7uxsuS5Kk6aXVwf7w8CX2etpft/cBi0Zs1wM81OLaJEma9lod7CuBc+r5c4DPjmj/vbp3/CnAo8OX7CVJ0tjNaOrAEfFxYDnwrIjoA/4MeDdwY0ScD/wAeG29+U3AK4D7gO3AeU3VJUlSyRoL9sx8/X5WvXyUbRO4qKlaJEk6XEyVznOSJGkSGOySJBXEYJckqSAGuyRJBTHYJUkqiMEuSVJBDHZJkgpisEuSVBCDXZKkghjskiQVxGCXJKkgBrskSQUx2CVJKojBLklSQQx2SZIKYrBLklQQg12SpIIY7JIkFcRglySpIAa7JEkFMdglSSqIwS5JUkEMdkmSCmKwS5JUEINdkqSCGOySJBXEYJckqSAGuyRJBTHYJUkqiMEuSVJBDHZJkgpisEuSVBCDXZKkghjskiQVxGCXJKkgBrskSQUx2CVJKojBLklSQQx2SZIKYrBLklQQg12SpILMaMcPjYgHgG3AELA7M3sjYj7wCeBY4AHgP2fmlnbUJ0nSdNXOM/ZfycylmdlbL18C3JqZxwO31suSJGkcptKl+BXAdfX8dcCr2liLJEnTUruCPYGbI+KbEXFB3XZ0Zm4GqKcLRtsxIi6IiDURsWZgYKBF5UqSND205R47cGpmPhQRC4BbImLDWHfMzKuBqwF6e3uzqQIlSZqO2hLsmflQPe2PiM8AJwMPR8Qxmbk5Io4B+ttR26T47i3w1Sth6/dh7s/ASy+GE85od1WSpMNAyy/FR8TTI2LO8Dzwq8A9wErgnHqzc4DPtrq2SfHdW+CzF0HfGnhsczX97EVVuyRJDWvHGfvRwGciYvjnfywzvxARdwA3RsT5wA+A17ahtom79XLY8R8QHXDEDMgnq+VbL/esXZLUuJYHe2ZuBF4wSvt/AC9vdT2T7kffA46AI+qLIXEEDGXdLklSs9rVea5c8ZP/jGiLfZokSWrCVPoeexnm/yzkEDz5JGTW06GqXZKkhhnsk+30d0LX/OoS/JND1bRrftUuSVLDDPbJdsIZsOID0NMLR/10NV3xATvOSZJawnvsTTjhDINcktQWnrFLklQQg12SpIIY7JIkFcRglySpIAa7JEkFMdglSSqIwS5JUkEMdkmSCmKwS5JUEINdkqSCGOySJBXEYJckqSAGuyRJBTHYJUkqiMEuSVJBDHZJkgpisEuSVBCDXZKkghjskiQVxGCXJKkgBrskSQUx2CVJKojBLklSQQx2SZIKMqPdBUiSpqZtq1fzyDUfZrCvj86eHuaf/0bmLFvW7rJ0EJ6xS5L2sW31ah6+4s/ZPTDAEc94BrsHBnj4ij9n2+rV7S5NB2GwS5L28cg1HyZmzuSIri4ioprOnMkj13y43aXpIAx2SdI+Bvv6iFmz9miLWbMY7OtrU0UaK4NdkrSPzp4ecufOPdpy5046e3raVJHGymCXJO1j/vlvJHft4skdO8jMarprF/PPf2O7S9NBGOySpH3MWbaMoy97BzO6u3ny0UeZ0d3N0Ze9w17x04Bfd5MkjWrOsmUG+TTkGbskSQUx2CVJKojBLklSQQx2SZIKMuWCPSLOjIjvRMR9EXFJu+uRJGk6mVLBHhEdwPuBs4CTgNdHxEntrUqSpOljSgU7cDJwX2ZuzMxdwA3AijbXJEnStDHVgn0hsGnEcl/d9hMRcUFErImINQMDAy0tTpKkqW6qBXuM0pZ7LGRenZm9mdnb3d3dorIkSZoeplqw9wGLRiz3AA+1qRZJkqadqRbsdwDHR8RxETETeB2wss01SZI0bUypZ8Vn5u6IeBPwRaAD+HBmrmtzWZIkTRtTKtgBMvMm4KZ21yFJ0nQ01S7FS5KkCTDYJUkqSGTmwbeaoiJiAPh+u+uYwp4F/KjdRUxzvocT53s4caW9hz/KzDPbXUSppnWw68AiYk1m9ra7junM93DifA8nzvdQ4+GleEmSCmKwS5JUEIO9bFe3u4AC+B5OnO/hxPkeasy8xy5JUkE8Y5ckqSAGuyRJBTHYCxERiyLitohYHxHrIuLiun1+RNwSEd+rp/PaXetUFhEdEfGtiPhcvXxcRNxev3+fqAcn0n5ExNyI+GREbKg/iy/xMzg+EfGH9b/heyLi4xExy8+hxsNgL8du4K2Z+VzgFOCiiDgJuAS4NTOPB26tl7V/FwPrRyz/JfC++v3bApzflqqmjyuBL2TmicALqN5LP4NjFBELgTcDvZn5PKrBsF6Hn0ONg8FeiMzcnJl31vPbqP6HuhBYAVxXb3Yd8Kr2VDj1RUQP8OvAh+rlAE4DPllv4vt3ABFxFLAMuAYgM3dl5lb8DI7XDKArImYAs4HN+DnUOBjsBYqIY4EXArcDR2fmZqjCH1jQvsqmvL8B/gR4sl5+JrA1M3fXy31UfyxpdIuBAeAj9e2MD0XE0/EzOGaZ+SDwHuAHVIH+KPBN/BxBJxlwAAADL0lEQVRqHAz2wkTEkcCngLdk5mPtrme6iIhXAv2Z+c2RzaNs6vdD928G8CLgg5n5QuDHeNl9XOr+ByuA44CfBp4OnDXKpn4OtV8Ge0EiopMq1D+amZ+umx+OiGPq9ccA/e2qb4o7FTg7Ih4AbqC69Pk3wNz6kihAD/BQe8qbFvqAvsy8vV7+JFXQ+xkcu9OB+zNzIDMHgU8DL8XPocbBYC9EfT/4GmB9Zr53xKqVwDn1/DnAZ1td23SQmW/PzJ7MPJaqs9KXMvN3gNuA36w38/07gMz8IbApIn6ubno5cC9+BsfjB8ApETG7/jc9/B76OdSY+eS5QkTELwFfBu7mqXvEl1LdZ78ReDbV/zRem5mPtKXIaSIilgNvy8xXRsRiqjP4+cC3gN/NzCfaWd9UFhFLqTofzgQ2AudRnUD4GRyjiHgn8FtU33T5FvD7VPfU/RxqTAx2SZIK4qV4SZIKYrBLklQQg12SpIIY7JIkFcRglySpIAa71ICIyIj4pxHLMyJiYHjUuAkc99yI+PuJVyipVAa71IwfA8+LiK56+QzgwfEcICoT+jc64mllkg4TBrvUnM9TjRYH8Hrg48MrIuLyiHjbiOV7IuLY+rU+Ij4A3AksiojzIuK7EfFvVI++Hd6nOyI+FRF31K9TRxz76oi4Gbi+Bb+npCnEYJeacwPwuoiYBfw81VMAx+LngOvrgVR2Ae+kCvQzgJNGbHcl1Rjdvwi8hnq42dovACsy87cn9itImm68TCc1JDPvqofQfT1w0zh2/X5mfr2efzGwKjMHACLiE8AJ9brTgZOqR4oDcFREzKnnV2bmjgmUL2maMtilZq2kGl97OdX47sN2s+cVs1kj5n+81zH299znI4CX7B3gddDvfQxJhwkvxUvN+jBwRWbevVf7A1RDmhIRL6Iaf3s0twPLI+KZ9bC8rx2x7mbgTcML9QAskg5zBrvUoMzsy8wrR1n1KWB+RKwF/ivw3f3svxm4HPga8H+pOtQNezPQGxF3RcS9wB9MZu2SpidHd5MkqSCesUuSVBCDXZKkghjskiQVxGCXJKkgBrskSQUx2CVJKojBLklSQf4/zowKx6myU60AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 514x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot( 'Murder','Assault',  data=df,\n",
    "        hue = 'Crime_clusters',\n",
    "        fit_reg=False, height = 6 );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=X\n",
    "clusters = KMeans(4);\n",
    "clusters.fit(X);\n",
    "plt.figure(figsize=(7, 7))\n",
    "clusters.labels_;\n",
    "centers = np.array(clusters.cluster_centers_)\n",
    "plt.scatter(centers[:,0], centers[:,1], marker=\"x\", color='r')\n",
    "c=plt.scatter(data.iloc[:,0],data.iloc[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot( 'Murder','Rape',  data=df,\n",
    "        hue = 'Crime_clusters',\n",
    "        fit_reg=False, height = 6 );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>Rape</th>\n",
       "      <th>UrbanPop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Murder</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.379260</td>\n",
       "      <td>0.583608</td>\n",
       "      <td>-0.573475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assault</th>\n",
       "      <td>-0.379260</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.435716</td>\n",
       "      <td>0.625214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rape</th>\n",
       "      <td>0.583608</td>\n",
       "      <td>-0.435716</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.426508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UrbanPop</th>\n",
       "      <td>-0.573475</td>\n",
       "      <td>0.625214</td>\n",
       "      <td>-0.426508</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Murder   Assault      Rape  UrbanPop\n",
       "Murder    1.000000 -0.379260  0.583608 -0.573475\n",
       "Assault  -0.379260  1.000000 -0.435716  0.625214\n",
       "Rape      0.583608 -0.435716  1.000000 -0.426508\n",
       "UrbanPop -0.573475  0.625214 -0.426508  1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variables_correlation = df[['Murder', 'Assault', 'Rape', 'UrbanPop']]\n",
    "variables_correlation.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Unnamed: 0</th>\n",
       "      <th>State</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>Rape</th>\n",
       "      <th>Total</th>\n",
       "      <th>Crime_clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>17.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>124.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>New York</td>\n",
       "      <td>45.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>145.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>190.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>218.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>58.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>236.0</td>\n",
       "      <td>21.2</td>\n",
       "      <td>270.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>44.5</td>\n",
       "      <td>317.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     State  UrbanPop  Murder  Assault  Rape  Total  \\\n",
       "8           8   Georgia      17.0    88.0     12.0  78.0  124.5   \n",
       "7           7  New York      45.0    12.0     44.0   5.2  145.8   \n",
       "3           3  Arkansas      50.0     8.8    190.0  19.5  218.3   \n",
       "0           0   Alabama      58.0    13.2    236.0  21.2  270.4   \n",
       "1           1    Alaska      48.0    10.0    263.0  44.5  317.5   \n",
       "\n",
       "   Crime_clusters  \n",
       "8               3  \n",
       "7               1  \n",
       "3               0  \n",
       "0               0  \n",
       "1               0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = df.sort_values(\"Total\", ascending=True)\n",
    "df_total= pd.DataFrame(stats)\n",
    "\n",
    "df_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(16, 10))\n",
    "\n",
    "# Load the dataset\n",
    "stats = df.sort_values([ \"Crime_clusters\", \"UrbanPop\"], ascending=True)\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.barplot(y=\"State\", x=\"UrbanPop\", data=stats)\n",
    "\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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