From 70d90906467a31a248184171b598bc7b0895b119 Mon Sep 17 00:00:00 2001
From: hirlekham28 <125371985+hirlekham28@users.noreply.github.com>
Date: Mon, 20 Feb 2023 19:17:07 +0530
Subject: [PATCH 1/3] Delete task_1.ipynb

---
 task_1.ipynb | 2416 --------------------------------------------------
 1 file changed, 2416 deletions(-)
 delete mode 100644 task_1.ipynb

diff --git a/task_1.ipynb b/task_1.ipynb
deleted file mode 100644
index 3ae541e..0000000
--- a/task_1.ipynb
+++ /dev/null
@@ -1,2416 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "860a25f4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.decomposition import PCA\n",
-    "from sklearn.cluster import KMeans\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import os \n",
-    "import seaborn as sns\n",
-    "import datetime as dt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da59500d",
-   "metadata": {},
-   "source": [
-    "# Read first data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "559fd892",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file_1 = os.path.join(os.getcwd(), \"miles-driven.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "874b86bc",
-   "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|million_miles_annually</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Alabama|64914</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Alaska|4593</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Arizona|59575</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Arkansas|32953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>California|320784</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  state|million_miles_annually\n",
-       "0                Alabama|64914\n",
-       "1                  Alaska|4593\n",
-       "2                Arizona|59575\n",
-       "3               Arkansas|32953\n",
-       "4            California|320784"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_1 = pd.read_csv(file_1)\n",
-    "df_1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ac97082",
-   "metadata": {},
-   "source": [
-    "# let's split our column"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cb080139",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df_1\n",
-    "df_1 = df_1[\"state|million_miles_annually\"].str.split(pat='|',expand=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "df2aeb14",
-   "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>0</th>\n",
-       "      <th>1</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>64914</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>4593</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Arizona</td>\n",
-       "      <td>59575</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Arkansas</td>\n",
-       "      <td>32953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>California</td>\n",
-       "      <td>320784</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            0       1\n",
-       "0     Alabama   64914\n",
-       "1      Alaska    4593\n",
-       "2     Arizona   59575\n",
-       "3    Arkansas   32953\n",
-       "4  California  320784"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9b46bf4e",
-   "metadata": {},
-   "source": [
-    "# change name of columns\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1b812dc2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_1.rename(columns={0: 'state', 1: 'million_miles_annually'}, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "be48abb1",
-   "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>million_miles_annually</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>64914</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>4593</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Arizona</td>\n",
-       "      <td>59575</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Arkansas</td>\n",
-       "      <td>32953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>California</td>\n",
-       "      <td>320784</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        state million_miles_annually\n",
-       "0     Alabama                  64914\n",
-       "1      Alaska                   4593\n",
-       "2     Arizona                  59575\n",
-       "3    Arkansas                  32953\n",
-       "4  California                 320784"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "942c2fd4",
-   "metadata": {},
-   "source": [
-    "# Read second data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7ada0e26",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df_2\n",
-    "file_2 = os.path.join(os.getcwd(), \"road-accidents.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "e32a992f",
-   "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>##### LICENSE #####</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td># This data set is modified from the original ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td># and it is released under CC BY 4.0 (https://...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>##### COLUMN ABBREVIATIONS #####</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td># drvr_fatl_col_bmiles = Number of drivers inv...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td># perc_fatl_speed = Percentage Of Drivers Invo...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td># perc_fatl_alcohol = Percentage Of Drivers In...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td># perc_fatl_1st_time = Percentage Of Drivers I...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>##### DATA BEGIN #####</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>state|drvr_fatl_col_bmiles|perc_fatl_speed|per...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Alabama|18.8|39|30|80</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 ##### LICENSE #####\n",
-       "0  # This data set is modified from the original ...\n",
-       "1  # and it is released under CC BY 4.0 (https://...\n",
-       "2                   ##### COLUMN ABBREVIATIONS #####\n",
-       "3  # drvr_fatl_col_bmiles = Number of drivers inv...\n",
-       "4  # perc_fatl_speed = Percentage Of Drivers Invo...\n",
-       "5  # perc_fatl_alcohol = Percentage Of Drivers In...\n",
-       "6  # perc_fatl_1st_time = Percentage Of Drivers I...\n",
-       "7                             ##### DATA BEGIN #####\n",
-       "8  state|drvr_fatl_col_bmiles|perc_fatl_speed|per...\n",
-       "9                              Alabama|18.8|39|30|80"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_2 = pd.read_csv(file_2)\n",
-    "df_2.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "0f75d251",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([['# This data set is modified from the original at fivethirtyeight (https://github.com/fivethirtyeight/data/tree/master/bad-drivers)'],\n",
-       "       ['# and it is released under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)'],\n",
-       "       ['##### COLUMN ABBREVIATIONS #####'],\n",
-       "       ['# drvr_fatl_col_bmiles = Number of drivers involved in fatal collisions per billion miles (2011)'],\n",
-       "       ['# perc_fatl_speed = Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding (2009)'],\n",
-       "       ['# perc_fatl_alcohol = Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired (2011)'],\n",
-       "       ['# perc_fatl_1st_time = Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents (2011)'],\n",
-       "       ['##### DATA BEGIN #####'],\n",
-       "       ['state|drvr_fatl_col_bmiles|perc_fatl_speed|perc_fatl_alcohol|perc_fatl_1st_time']],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "another_data = df_2[0:9].values\n",
-    "another_data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae2c2972",
-   "metadata": {},
-   "source": [
-    "# change name of columns\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "b5f0a554",
-   "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|drvr_fatl_col_bmiles|perc_fatl_speed|perc_fatl_alcohol|perc_fatl_1st_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td># This data set is modified from the original ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td># and it is released under CC BY 4.0 (https://...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>##### COLUMN ABBREVIATIONS #####</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td># drvr_fatl_col_bmiles = Number of drivers inv...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td># perc_fatl_speed = Percentage Of Drivers Invo...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td># perc_fatl_alcohol = Percentage Of Drivers In...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td># perc_fatl_1st_time = Percentage Of Drivers I...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>##### DATA BEGIN #####</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>state|drvr_fatl_col_bmiles|perc_fatl_speed|per...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Alabama|18.8|39|30|80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Alaska|18.1|41|25|94</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Arizona|18.6|35|28|96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Arkansas|22.4|18|26|95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>California|12|35|28|89</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Colorado|13.6|37|28|95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Connecticut|10.8|46|36|82</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>Delaware|16.2|38|30|99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>District of Columbia|5.9|34|27|100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Florida|17.9|21|29|94</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>Georgia|15.6|19|25|93</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>Hawaii|17.5|54|41|87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>Idaho|15.3|36|29|98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>Illinois|12.8|36|34|96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>Indiana|14.5|25|29|95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>Iowa|15.7|17|25|87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Kansas|17.8|27|24|85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Kentucky|21.4|19|23|76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>Louisiana|20.5|35|33|98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Maine|15.1|38|30|84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Maryland|12.5|34|32|99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Massachusetts|8.2|23|35|80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>Michigan|14.1|24|28|77</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>Minnesota|9.6|23|29|88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>Mississippi|17.6|15|31|100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>Missouri|16.1|43|34|84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>Montana|21.4|39|44|85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>Nebraska|14.9|13|35|90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>Nevada|14.7|37|32|99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>New Hampshire|11.6|35|30|83</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>New Jersey|11.2|16|28|78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>New Mexico|18.4|19|27|98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>New York|12.3|32|29|80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>North Carolina|16.8|39|31|81</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>North Dakota|23.9|23|42|86</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>Ohio|14.1|28|34|82</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>Oklahoma|19.9|32|29|94</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>Oregon|12.8|33|26|90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>Pennsylvania|18.2|50|31|88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>Rhode Island|11.1|34|38|79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>South Carolina|23.9|38|41|81</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>South Dakota|19.4|31|33|86</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>Tennessee|19.5|21|29|81</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>Texas|19.4|40|38|87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>Utah|11.3|43|16|96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>Vermont|13.6|30|30|95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>Virginia|12.7|19|27|88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>Washington|10.6|42|33|86</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>West Virginia|23.8|34|28|87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>Wisconsin|13.8|36|33|84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>59</th>\n",
-       "      <td>Wyoming|17.4|42|32|90</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   state|drvr_fatl_col_bmiles|perc_fatl_speed|perc_fatl_alcohol|perc_fatl_1st_time\n",
-       "0   # This data set is modified from the original ...                             \n",
-       "1   # and it is released under CC BY 4.0 (https://...                             \n",
-       "2                    ##### COLUMN ABBREVIATIONS #####                             \n",
-       "3   # drvr_fatl_col_bmiles = Number of drivers inv...                             \n",
-       "4   # perc_fatl_speed = Percentage Of Drivers Invo...                             \n",
-       "5   # perc_fatl_alcohol = Percentage Of Drivers In...                             \n",
-       "6   # perc_fatl_1st_time = Percentage Of Drivers I...                             \n",
-       "7                              ##### DATA BEGIN #####                             \n",
-       "8   state|drvr_fatl_col_bmiles|perc_fatl_speed|per...                             \n",
-       "9                               Alabama|18.8|39|30|80                             \n",
-       "10                               Alaska|18.1|41|25|94                             \n",
-       "11                              Arizona|18.6|35|28|96                             \n",
-       "12                             Arkansas|22.4|18|26|95                             \n",
-       "13                             California|12|35|28|89                             \n",
-       "14                             Colorado|13.6|37|28|95                             \n",
-       "15                          Connecticut|10.8|46|36|82                             \n",
-       "16                             Delaware|16.2|38|30|99                             \n",
-       "17                 District of Columbia|5.9|34|27|100                             \n",
-       "18                              Florida|17.9|21|29|94                             \n",
-       "19                              Georgia|15.6|19|25|93                             \n",
-       "20                               Hawaii|17.5|54|41|87                             \n",
-       "21                                Idaho|15.3|36|29|98                             \n",
-       "22                             Illinois|12.8|36|34|96                             \n",
-       "23                              Indiana|14.5|25|29|95                             \n",
-       "24                                 Iowa|15.7|17|25|87                             \n",
-       "25                               Kansas|17.8|27|24|85                             \n",
-       "26                             Kentucky|21.4|19|23|76                             \n",
-       "27                            Louisiana|20.5|35|33|98                             \n",
-       "28                                Maine|15.1|38|30|84                             \n",
-       "29                             Maryland|12.5|34|32|99                             \n",
-       "30                         Massachusetts|8.2|23|35|80                             \n",
-       "31                             Michigan|14.1|24|28|77                             \n",
-       "32                             Minnesota|9.6|23|29|88                             \n",
-       "33                         Mississippi|17.6|15|31|100                             \n",
-       "34                             Missouri|16.1|43|34|84                             \n",
-       "35                              Montana|21.4|39|44|85                             \n",
-       "36                             Nebraska|14.9|13|35|90                             \n",
-       "37                               Nevada|14.7|37|32|99                             \n",
-       "38                        New Hampshire|11.6|35|30|83                             \n",
-       "39                           New Jersey|11.2|16|28|78                             \n",
-       "40                           New Mexico|18.4|19|27|98                             \n",
-       "41                             New York|12.3|32|29|80                             \n",
-       "42                       North Carolina|16.8|39|31|81                             \n",
-       "43                         North Dakota|23.9|23|42|86                             \n",
-       "44                                 Ohio|14.1|28|34|82                             \n",
-       "45                             Oklahoma|19.9|32|29|94                             \n",
-       "46                               Oregon|12.8|33|26|90                             \n",
-       "47                         Pennsylvania|18.2|50|31|88                             \n",
-       "48                         Rhode Island|11.1|34|38|79                             \n",
-       "49                       South Carolina|23.9|38|41|81                             \n",
-       "50                         South Dakota|19.4|31|33|86                             \n",
-       "51                            Tennessee|19.5|21|29|81                             \n",
-       "52                                Texas|19.4|40|38|87                             \n",
-       "53                                 Utah|11.3|43|16|96                             \n",
-       "54                              Vermont|13.6|30|30|95                             \n",
-       "55                             Virginia|12.7|19|27|88                             \n",
-       "56                           Washington|10.6|42|33|86                             \n",
-       "57                        West Virginia|23.8|34|28|87                             \n",
-       "58                            Wisconsin|13.8|36|33|84                             \n",
-       "59                              Wyoming|17.4|42|32|90                             "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_2.rename(columns={\"##### LICENSE #####\": 'state|drvr_fatl_col_bmiles|perc_fatl_speed|perc_fatl_alcohol|perc_fatl_1st_time'}, inplace=True)\n",
-    "df_2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e09dcf4f",
-   "metadata": {},
-   "source": [
-    "# drop first 9 rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "760f2e6c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RangeIndex(start=0, stop=60, step=1)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_2.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "e39d610c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_2.drop(axis = 0, index= df_2.index[:9], inplace= True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "a98f752a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_2.reset_index(drop= True, inplace= True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2aa70541",
-   "metadata": {},
-   "source": [
-    "# let's split our column"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "3072f887",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_2 = df_2[\"state|drvr_fatl_col_bmiles|perc_fatl_speed|perc_fatl_alcohol|perc_fatl_1st_time\"].str.split(pat='|',expand=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "b766e0d6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>18.8</td>\n",
-       "      <td>39</td>\n",
-       "      <td>30</td>\n",
-       "      <td>80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>18.1</td>\n",
-       "      <td>41</td>\n",
-       "      <td>25</td>\n",
-       "      <td>94</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Arizona</td>\n",
-       "      <td>18.6</td>\n",
-       "      <td>35</td>\n",
-       "      <td>28</td>\n",
-       "      <td>96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Arkansas</td>\n",
-       "      <td>22.4</td>\n",
-       "      <td>18</td>\n",
-       "      <td>26</td>\n",
-       "      <td>95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>California</td>\n",
-       "      <td>12</td>\n",
-       "      <td>35</td>\n",
-       "      <td>28</td>\n",
-       "      <td>89</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            0     1   2   3   4\n",
-       "0     Alabama  18.8  39  30  80\n",
-       "1      Alaska  18.1  41  25  94\n",
-       "2     Arizona  18.6  35  28  96\n",
-       "3    Arkansas  22.4  18  26  95\n",
-       "4  California    12  35  28  89"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_2.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "61ce5823",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_2.rename(columns={0: 'state', 1: 'drvr_fatl_col_bmiles', 2:\"perc_fatl_speed\" , 3:\"perc_fatl_alcohol\" , 4:\"perc_fatl_1st_time\"}, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "6554ff3c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>drvr_fatl_col_bmiles</th>\n",
-       "      <th>perc_fatl_speed</th>\n",
-       "      <th>perc_fatl_alcohol</th>\n",
-       "      <th>perc_fatl_1st_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>18.8</td>\n",
-       "      <td>39</td>\n",
-       "      <td>30</td>\n",
-       "      <td>80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>18.1</td>\n",
-       "      <td>41</td>\n",
-       "      <td>25</td>\n",
-       "      <td>94</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Arizona</td>\n",
-       "      <td>18.6</td>\n",
-       "      <td>35</td>\n",
-       "      <td>28</td>\n",
-       "      <td>96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Arkansas</td>\n",
-       "      <td>22.4</td>\n",
-       "      <td>18</td>\n",
-       "      <td>26</td>\n",
-       "      <td>95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>California</td>\n",
-       "      <td>12</td>\n",
-       "      <td>35</td>\n",
-       "      <td>28</td>\n",
-       "      <td>89</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        state drvr_fatl_col_bmiles perc_fatl_speed perc_fatl_alcohol  \\\n",
-       "0     Alabama                 18.8              39                30   \n",
-       "1      Alaska                 18.1              41                25   \n",
-       "2     Arizona                 18.6              35                28   \n",
-       "3    Arkansas                 22.4              18                26   \n",
-       "4  California                   12              35                28   \n",
-       "\n",
-       "  perc_fatl_1st_time  \n",
-       "0                 80  \n",
-       "1                 94  \n",
-       "2                 96  \n",
-       "3                 95  \n",
-       "4                 89  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_2.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "e404484f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>million_miles_annually</th>\n",
-       "      <th>drvr_fatl_col_bmiles</th>\n",
-       "      <th>perc_fatl_speed</th>\n",
-       "      <th>perc_fatl_alcohol</th>\n",
-       "      <th>perc_fatl_1st_time</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>64914</td>\n",
-       "      <td>18.8</td>\n",
-       "      <td>39</td>\n",
-       "      <td>30</td>\n",
-       "      <td>80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>4593</td>\n",
-       "      <td>18.1</td>\n",
-       "      <td>41</td>\n",
-       "      <td>25</td>\n",
-       "      <td>94</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Arizona</td>\n",
-       "      <td>59575</td>\n",
-       "      <td>18.6</td>\n",
-       "      <td>35</td>\n",
-       "      <td>28</td>\n",
-       "      <td>96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Arkansas</td>\n",
-       "      <td>32953</td>\n",
-       "      <td>22.4</td>\n",
-       "      <td>18</td>\n",
-       "      <td>26</td>\n",
-       "      <td>95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>California</td>\n",
-       "      <td>320784</td>\n",
-       "      <td>12</td>\n",
-       "      <td>35</td>\n",
-       "      <td>28</td>\n",
-       "      <td>89</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        state million_miles_annually drvr_fatl_col_bmiles perc_fatl_speed  \\\n",
-       "0     Alabama                  64914                 18.8              39   \n",
-       "1      Alaska                   4593                 18.1              41   \n",
-       "2     Arizona                  59575                 18.6              35   \n",
-       "3    Arkansas                  32953                 22.4              18   \n",
-       "4  California                 320784                   12              35   \n",
-       "\n",
-       "  perc_fatl_alcohol perc_fatl_1st_time  \n",
-       "0                30                 80  \n",
-       "1                25                 94  \n",
-       "2                28                 96  \n",
-       "3                26                 95  \n",
-       "4                28                 89  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_total = pd.merge(df_1, df_2, on='state')\n",
-    "df_total.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "331212bb",
-   "metadata": {},
-   "source": [
-    "# Check Nulls"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "fda232e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "state                     0\n",
-       "million_miles_annually    0\n",
-       "drvr_fatl_col_bmiles      0\n",
-       "perc_fatl_speed           0\n",
-       "perc_fatl_alcohol         0\n",
-       "perc_fatl_1st_time        0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_total.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "35b2f2e2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 51 entries, 0 to 50\n",
-      "Data columns (total 6 columns):\n",
-      " #   Column                  Non-Null Count  Dtype \n",
-      "---  ------                  --------------  ----- \n",
-      " 0   state                   51 non-null     object\n",
-      " 1   million_miles_annually  51 non-null     object\n",
-      " 2   drvr_fatl_col_bmiles    51 non-null     object\n",
-      " 3   perc_fatl_speed         51 non-null     object\n",
-      " 4   perc_fatl_alcohol       51 non-null     object\n",
-      " 5   perc_fatl_1st_time      51 non-null     object\n",
-      "dtypes: object(6)\n",
-      "memory usage: 2.8+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_total.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6320d7b",
-   "metadata": {},
-   "source": [
-    "# Change Dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "905fd9c6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_total = df_total.astype({'million_miles_annually': 'int64', 'drvr_fatl_col_bmiles': 'float64','perc_fatl_speed': 'int64', 'perc_fatl_alcohol': 'int64', 'perc_fatl_1st_time': 'int64'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "570bcdba",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 51 entries, 0 to 50\n",
-      "Data columns (total 6 columns):\n",
-      " #   Column                  Non-Null Count  Dtype  \n",
-      "---  ------                  --------------  -----  \n",
-      " 0   state                   51 non-null     object \n",
-      " 1   million_miles_annually  51 non-null     int64  \n",
-      " 2   drvr_fatl_col_bmiles    51 non-null     float64\n",
-      " 3   perc_fatl_speed         51 non-null     int64  \n",
-      " 4   perc_fatl_alcohol       51 non-null     int64  \n",
-      " 5   perc_fatl_1st_time      51 non-null     int64  \n",
-      "dtypes: float64(1), int64(4), object(1)\n",
-      "memory usage: 2.8+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_total.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "ebcc3e0d",
-   "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>million_miles_annually</th>\n",
-       "      <th>drvr_fatl_col_bmiles</th>\n",
-       "      <th>perc_fatl_speed</th>\n",
-       "      <th>perc_fatl_alcohol</th>\n",
-       "      <th>perc_fatl_1st_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>64914</td>\n",
-       "      <td>18.8</td>\n",
-       "      <td>39</td>\n",
-       "      <td>30</td>\n",
-       "      <td>80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>4593</td>\n",
-       "      <td>18.1</td>\n",
-       "      <td>41</td>\n",
-       "      <td>25</td>\n",
-       "      <td>94</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Arizona</td>\n",
-       "      <td>59575</td>\n",
-       "      <td>18.6</td>\n",
-       "      <td>35</td>\n",
-       "      <td>28</td>\n",
-       "      <td>96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Arkansas</td>\n",
-       "      <td>32953</td>\n",
-       "      <td>22.4</td>\n",
-       "      <td>18</td>\n",
-       "      <td>26</td>\n",
-       "      <td>95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>California</td>\n",
-       "      <td>320784</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>35</td>\n",
-       "      <td>28</td>\n",
-       "      <td>89</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Colorado</td>\n",
-       "      <td>46606</td>\n",
-       "      <td>13.6</td>\n",
-       "      <td>37</td>\n",
-       "      <td>28</td>\n",
-       "      <td>95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Connecticut</td>\n",
-       "      <td>31197</td>\n",
-       "      <td>10.8</td>\n",
-       "      <td>46</td>\n",
-       "      <td>36</td>\n",
-       "      <td>82</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Delaware</td>\n",
-       "      <td>9028</td>\n",
-       "      <td>16.2</td>\n",
-       "      <td>38</td>\n",
-       "      <td>30</td>\n",
-       "      <td>99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>District of Columbia</td>\n",
-       "      <td>3568</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>34</td>\n",
-       "      <td>27</td>\n",
-       "      <td>100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Florida</td>\n",
-       "      <td>191855</td>\n",
-       "      <td>17.9</td>\n",
-       "      <td>21</td>\n",
-       "      <td>29</td>\n",
-       "      <td>94</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  state  million_miles_annually  drvr_fatl_col_bmiles  \\\n",
-       "0               Alabama                   64914                  18.8   \n",
-       "1                Alaska                    4593                  18.1   \n",
-       "2               Arizona                   59575                  18.6   \n",
-       "3              Arkansas                   32953                  22.4   \n",
-       "4            California                  320784                  12.0   \n",
-       "5              Colorado                   46606                  13.6   \n",
-       "6           Connecticut                   31197                  10.8   \n",
-       "7              Delaware                    9028                  16.2   \n",
-       "8  District of Columbia                    3568                   5.9   \n",
-       "9               Florida                  191855                  17.9   \n",
-       "\n",
-       "   perc_fatl_speed  perc_fatl_alcohol  perc_fatl_1st_time  \n",
-       "0               39                 30                  80  \n",
-       "1               41                 25                  94  \n",
-       "2               35                 28                  96  \n",
-       "3               18                 26                  95  \n",
-       "4               35                 28                  89  \n",
-       "5               37                 28                  95  \n",
-       "6               46                 36                  82  \n",
-       "7               38                 30                  99  \n",
-       "8               34                 27                 100  \n",
-       "9               21                 29                  94  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_total.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ce136f4c",
-   "metadata": {},
-   "source": [
-    "# Visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "76e60ccf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "\n",
-    "sns.boxplot(y='million_miles_annually', data=df_total)\n",
-    "\n",
-    "## title and axis\n",
-    "plt.title('Distr of million_miles_annually', fontsize=15)\n",
-    "plt.xlabel('million_miles_annually', fontsize=15)\n",
-    "plt.ylabel('Values', fontsize=15)\n",
-    "plt.show()\n",
-    "\n",
-    "## Let's make a Histogram\n",
-    "plt.figure(figsize=(10, 6))    ## figure shape\n",
-    "\n",
-    "sns.histplot(x='million_miles_annually', data=df_total, bins=50, kde=True)\n",
-    "\n",
-    "## title and axis\n",
-    "plt.title('Distr. of million_miles_annually Dataset', fontsize=15)\n",
-    "plt.xlabel('million_miles_annually', fontsize=15)\n",
-    "plt.ylabel('Frequency', fontsize=15)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "c34426af",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "\n",
-    "sns.boxplot(y='drvr_fatl_col_bmiles', data=df_total)\n",
-    "\n",
-    "## title and axis\n",
-    "plt.title('Distr of drvr_fatl_col_bmiles', fontsize=15)\n",
-    "plt.xlabel('drvr_fatl_col_bmiles', fontsize=15)\n",
-    "plt.ylabel('Values', fontsize=15)\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "## Let's make a Histogram\n",
-    "plt.figure(figsize=(10, 6))    ## figure shape\n",
-    "\n",
-    "sns.histplot(x='drvr_fatl_col_bmiles', data=df_total, bins=50, kde=True)\n",
-    "\n",
-    "## title and axis\n",
-    "plt.title('Distr. of drvr_fatl_col_bmiles Dataset', fontsize=15)\n",
-    "plt.xlabel('drvr_fatl_col_bmiles', fontsize=15)\n",
-    "plt.ylabel('Frequency', fontsize=15)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "09a2fbe7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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tWx24qi4GLu5f3wgc2qoWSZKkVjaXnjFJkqQ5yTAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNdRs0leNb9myZaxZs6Z1GdKcM/L3bunSpY0rkeaeRYsWsWTJktZlNGEY2wytWbOGy6/8Fvc9YkHrUqQ5Zat7CoBV37+ucSXS3DLvjptal9CUYWwzdd8jFnDnvs9rXYYkSdNu/urzWpfQlNeMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDU01Az8SbYG5lXV3QPrDgOeDHypqi6bpvokSZJmtWG/DulfgJuBPwBI8ifAB4C7gXlJjqqqc6alQkmSpFls2GHKpwGDXxz1euCUqpoPfBx4y1QXJkmSNBcMG8YeDVwLkOSXgMcAf9Nv+wzdcKUkSZImadgwdh2wsH99OHB1VX2vX54P3D/FdUmSJM0Jw14z9hng3Ul+Gfh94IMD2w4AvjvVhUmSJM0Fw4axE4BbgKcCHwH+emDbr9Jd4C9JkqRJGiqMVdW9wNvH2XbUlFYkSZI0h0xq0tckv5HkL5KclmSvft0hSR4zPeVJkiTNbsNO+robcDbdkORa4HF0d1P+kO4asruAV01PiZIkSbPXsD1jy4DtgH37Rwa2XQgcOsV1SZIkzQnDXsB/OHBMVa1JMm/Uth8Be05tWZIkSXPDZK4Zu2+c9TsDd05BLZIkSXPOsGHsP4Elo3rFqn/+A+CiKa1KkiRpjhh2mPKNwCXAlcCZdEHslUn2B/an++5KSZIkTdJQPWNVdSXdnZQrgWPphiyPAv4H+LWq+s50FShJkjSbDdszRv9dlC+fxlokSZLmnKHDmGbOunXrmHfHzcxffV7rUiRJmnbz7riRdevubV1GM8NO+vp1Hrhgf0xVdeCUVCRJkjSHDNszdhUPDmMLgIPoprVYMZVFzXV77rkn1969NXfu+7zWpUiSNO3mrz6PPffcrXUZzQz7ReHHjrU+yXZ0X5P0X1NYkyRJ0pwxqS8KH62qbgNOAd4yNeVIkiTNLQ8pjPV2BHaagv1IkiTNOcNewD/WxUvbAE8C/hT4wlQWJUmSNFcMewH/OXQX8GfU+p8CZwGvmcqiJEmS5ophw9jjxlh3F7Chqiac8kKSJEnjG/ZuyqunuxBJkqS5aNwwluTJk9lRVf33Qy9HkiRpbpmoZ+xKNjLrfi99u3lTUpEkSdIcMlEYe9aMVSFJkjRHjRvGquqLM1mIJEnSXDTs3ZQ/k2QrYNvR66vqjimpSJIkaQ4Zagb+dN6YZA3d3GK3jvGQJEnSJA37dUh/ApwA/C3dBfvvBN4OfAdYCxw/HcVJkiTNdsOGsVcCbwNO7pf/tapOAvYDVgP7TENtkiRJs96wYexxwOVVdR/dMOWOAFV1P/Bh4JhpqU6SJGmWGzaM3Qhs17/+IXDAwLadgPlTWZQkSdJcMdEM/A+rqp/2i5cCTwXOAz4JnJhkAXAP8GpgxXQXKkmSNBtNNLXFtUk+C3yK7mL93fv1f0U3THksXY/YBcCS6StRkiRp9poojP0z8JvAccB1wKeT3F5VXwOW9g9JkiQ9BONeM1ZVrwH2BH6dbnjyd4EvJ/l+kr9Mst8M1ShJkjRrTXgBf1XdX1UXVtUr6IYpj6C7fmwJcEWSK5O8OcnjZ6BWSZKkWWfYuympqnur6pyqejmwK3A03RxjI5O/SpIkaZKGDmOjHAAcAjy938cPp6wiSZKkOWToMJbkgCTvTvIDuqHK3wLOAA6uqqGGKZNsm+RrSb6R5KokJ/XrFyS5IMl3++edNuXDSJIkbWkmupuSJE8CXkYXvPYBbgbOpLvT8qJ+Bv7JuBt4dlXdluRhwCVJ/h04ClhRVe9KcgLd92C+cZL7liRJ2uJMNOnrFXTfPXkncA5dOPr3qrpnUw9WVQXc1i8+rH8U3Y0Bz+zXLwcuxjAmSZLmgIl6xq4G3gWcVVW3T9UBk8wDVgGLgA9V1VeT7FZV6wGqan2SXcd57/HA8QB77bXXVJUkSZLUzLhhrKpeOB0H7L9s/ClJdgTOTLL/JN57GnAawOLFi2s66pMkSZpJm3o35UNWVT+hG448HLguyR4A/fOGVnVJkiTNpBkNY0l26XvESDIfeA7dXGVnA8f0zY4BzprJuiRJklqZ8G7KabAHsLy/bmwr4NNVdU6SL9N99+VxdHOWHT3DdUmSJDUxo2Gsqq6gmzB29PobgUNnshZJkqTNQbNrxiRJkmQYkyRJasowJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLU0NatC9DY5t1xE/NXn9e6DGlO2equWwC4f9tHNa5Emlvm3XETsFvrMpoxjG2GFi1a1LoEaU5as+ZWABY9fu7+UpDa2G1O/+4zjG2GlixZ0roEaU5aunQpAKeeemrjSiTNJV4zJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLU0IyGsSS/kOQLSb6V5KokS/v1C5JckOS7/fNOM1mXJElSKzPdM3Yv8OdV9STgacCrkzwZOAFYUVX7ACv6ZUmSpFlvRsNYVa2vqsv617cC3wL2BI4AlvfNlgNHzmRdkiRJrTS7ZizJQuAA4KvAblW1HrrABuw6znuOT7Iyycrrr79+xmqVJEmaLk3CWJLtgM8Cr62qW4Z9X1WdVlWLq2rxLrvsMn0FSpIkzZAZD2NJHkYXxP6pqj7Xr74uyR799j2ADTNdlyRJUgszfTdlgL8FvlVV7xvYdDZwTP/6GOCsmaxLkiSpla1n+HgHAy8Hvpnk8n7dm4F3AZ9OchzwQ+DoGa5LkiSpiRkNY1V1CZBxNh86k7VIkiRtDpyBX5IkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDW3dugBpNlm2bBlr1qxpXYY20cjPbunSpY0r0aZatGgRS5YsaV2GNCmGMUnqzZ8/v3UJkuYgw5g0hfwfuSRpsrxmTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaSlW1rmGTJLkeuLp1HZJmnZ2BG1oXIWnW2buqdhlrwxYbxiRpOiRZWVWLW9chae5wmFKSJKkhw5gkSVJDhjFJ+nmntS5A0tziNWOSJEkN2TMmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZoVkjwpyX8muT1JJVk45PsOS/LaMdafnmTlVNfZUpLXJHE+I2kzYxiTNFu8B9gReBFwELB+yPcdBrx2ekqSpI3bunUBkua2JA8D7q+q+x7irvYFzq6qFVNQliTNGHvGJA1lZNguyZFJVie5K8klSZ480GarJCckWZPk7iTfSXLMqP1cnOSMJMcn+R5wF/CYftsrk3yz3/d1fbsdNlLXwn7o7QnAn/ZDlBf3256f5IIkG5LckuQrSQ4beO+JwJ8De/fvqySnb+L5eVGSVf0w6Y+TfDXJMwa2V5I/S3JqkpuS/CTJsiTbjNrPXkk+1be5I8n5SX5xVJttk5yc5H/68/yNJM8b1ebhST7YH+emJO8HHrYpn03S9LJnTNJk7A28D/gL4E7gJOD8JPtU1V3AMuAY4O3AZcBzgU8kubGqzhnYz8F04emNwB3AzUn+b/++DwOvBx4BPB/YDrh5gprW0w1Lnglc1NdwS7/tccC/Ae8F7gd+A/j3JIdU1aXAx4F9gGcDL+7fc/1kT0qSJwBnAKf2tW8L/CqwYFTTPwe+AvwOsB/wTrow+vp+PwuAS4AbgT+iOzcnABcmeWJV3dnv5wzgQOBtwPeAlwJnJ1lcVZf3bd4FvAJ4C/DfwCuBoyf72STNgKry4cOHj40+gNOBAp4+sG5v4F664LCILvAcM+p9fw98fWD5Yrogt/vAuh3pgsf7HkJ9a4H3TrB9K7r/gJ4PfGJg/XuBteN83pVDHvslwI0baVPAamCrgXVv6T/3gn75HXRBbMFAm53owuir++VD+309Y9T+vwR8pn/96P4cv3HU51/d/bPf/s+TDx8+Hng4TClpMjZU1X+NLFTV1cAqul6aQ+nC2JlJth55ACuApySZN7CfVVV17cDyQcB84O+mstgkj02yPMk6utD4U7oL9p84lccBvgns0B/rsCSPHKfdWVV1/8Dy5+g+9/798nOAC4BbBs7frXTnePFAm2uBS8c4zyNtfomud+6skQP1x/3ZsqTNh8OUkiZjwzjr9gB2BuYx/pDiHsCP+tfXjdr26P552DsgNyrJVsDZwPbAW4E1wO10Q6G7TtVxAKrq20mOoBtSPA/4aZIzgaVVNTjsOfr8jSzv0T/vDDwN+K0xDrNioM3udMFytJGbIHbfyPEkbUYMY5ImY6wQsytwFXATXe/TwXQ9ZKMNBoHRc13d2D/vAdzwEGscsQg4APiNqvr8yMok86do/z+nqs4Fzu1vOHg+8AG669deNtBs9PkbWR4JoTfRBch3jHGIWwfarAOOnKCckV7HXfv24x1f0mbAMCZpMnZN8vSRocokewG/Qje8+AW6nrEdquqCSe73y3TXOB0DvG6Kah0JXXePrEiyN11YvGKg3T10Q3pToqpuBj7Z30l50KjNRyR508BQ5VF0n/vKfnkF3cX4V9UDF+uPtoLuRoDbqmr1OG2+SXdjwBF014mN9BQesQkfSdI0M4xJmowbgH9IMnI35dvperxOr6q7kvwN8KkkJwMr6ULOfsATq+oV4+20qn6S5B3AO/upHs4DHk7Xw3RSVa3bhFpX0w2LntLXuz3d3Z+j97Ua2C3JsXSh6IaqWjuZAyX5Q7rg9XngGro7NI+mu3lh0PbAZ5J8jO68vBX4YFWN9F69D/hd4KIky/padwOeAVxSVf9Md03Z+cAFSd5N1yv5KOApwLZV9aaqujHJacBJSe7t27yS7s5USZsZw5ikybga+Cu6aRP2pgtc/6e6aS0AXg18h+4X/9vpppj4b+BvN7bjqvrrJDcBS4E/BH5Md4fgrRO+cfz93Z3kKOBDdFNB/IhuKoln8sAF8wCfBp4FnAzsAiwHjp3k4a6gm/n/fXTTWawHPkYXtgadAjwe+Ge6uxs/Drx5oOYbkjytr/P9dHeZrqeb7uKKvk31n+vNdN8csBfdUOTldMOiI95AN6/YW+mGjf+xr++USX42SdMsVX5NmaSN6ydD3b+qFm+srR6sn5h2SVV9sHUtkjYvTm0hSZLUkMOUkjZ7/Txa47l/1Nxd03H8eUDG2Vz10L9XU9Ic5jClpM1akoXADyZosryqjp3mGtbSXSM3lqurauF0Hl/S7GbPmKTN3TXAUyfYPlXzkk3khXR3d47l7nHWS9JQ7BmTJElqyAv4JUmSGjKMSZIkNWQYkyRJasgwJkmS1ND/BxcWES1An3fVAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "\n",
-    "sns.boxplot(y='perc_fatl_speed', data=df_total)\n",
-    "\n",
-    "## title and axis\n",
-    "plt.title('Distr of perc_fatl_speed', fontsize=15)\n",
-    "plt.xlabel('perc_fatl_speed', fontsize=15)\n",
-    "plt.ylabel('Values', fontsize=15)\n",
-    "plt.show()\n",
-    "\n",
-    "## Let's make a Histogram\n",
-    "plt.figure(figsize=(10, 6))    ## figure shape\n",
-    "\n",
-    "sns.histplot(x='perc_fatl_speed', data=df_total, bins=50, kde=True)\n",
-    "\n",
-    "## title and axis\n",
-    "plt.title('Distr. of perc_fatl_speed Dataset', fontsize=15)\n",
-    "plt.xlabel('perc_fatl_speed', fontsize=15)\n",
-    "plt.ylabel('Frequency', fontsize=15)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "7e227d2b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "\n",
-    "sns.boxplot(y='perc_fatl_alcohol', data=df_total)\n",
-    "\n",
-    "## title and axis\n",
-    "plt.title('Distr of perc_fatl_alcohol', fontsize=15)\n",
-    "plt.xlabel('perc_fatl_alcohol', fontsize=15)\n",
-    "plt.ylabel('Values', fontsize=15)\n",
-    "plt.show()\n",
-    "\n",
-    "## Let's make a Histogram\n",
-    "plt.figure(figsize=(10, 6))    ## figure shape\n",
-    "\n",
-    "sns.histplot(x='perc_fatl_alcohol', data=df_total, bins=50, kde=True)\n",
-    "\n",
-    "## title and axis\n",
-    "plt.title('Distr. of perc_fatl_alcohol Dataset', fontsize=15)\n",
-    "plt.xlabel('perc_fatl_alcohol', fontsize=15)\n",
-    "plt.ylabel('Frequency', fontsize=15)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "26342e5e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "\n",
-    "sns.boxplot(y='perc_fatl_1st_time', data=df_total)\n",
-    "\n",
-    "## title and axis\n",
-    "plt.title('Distr of perc_fatl_1st_time', fontsize=15)\n",
-    "plt.xlabel('perc_fatl_1st_time', fontsize=15)\n",
-    "plt.ylabel('Values', fontsize=15)\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "## Let's make a Histogram\n",
-    "plt.figure(figsize=(10, 6))    ## figure shape\n",
-    "\n",
-    "sns.histplot(x='perc_fatl_1st_time', data=df_total, bins=50, kde=True)\n",
-    "\n",
-    "## title and axis\n",
-    "plt.title('Distr. of perc_fatl_1st_time Dataset', fontsize=15)\n",
-    "plt.xlabel('perc_fatl_1st_time', fontsize=15)\n",
-    "plt.ylabel('Frequency', fontsize=15)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "cb61d85d",
-   "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>million_miles_annually</th>\n",
-       "      <th>drvr_fatl_col_bmiles</th>\n",
-       "      <th>perc_fatl_speed</th>\n",
-       "      <th>perc_fatl_alcohol</th>\n",
-       "      <th>perc_fatl_1st_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>51.000000</td>\n",
-       "      <td>51.000000</td>\n",
-       "      <td>51.000000</td>\n",
-       "      <td>51.000000</td>\n",
-       "      <td>51.00000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>57851.019608</td>\n",
-       "      <td>15.790196</td>\n",
-       "      <td>31.725490</td>\n",
-       "      <td>30.686275</td>\n",
-       "      <td>88.72549</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>59898.414088</td>\n",
-       "      <td>4.122002</td>\n",
-       "      <td>9.633438</td>\n",
-       "      <td>5.132213</td>\n",
-       "      <td>6.96011</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>3568.000000</td>\n",
-       "      <td>5.900000</td>\n",
-       "      <td>13.000000</td>\n",
-       "      <td>16.000000</td>\n",
-       "      <td>76.00000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>17450.000000</td>\n",
-       "      <td>12.750000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>28.000000</td>\n",
-       "      <td>83.50000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>46606.000000</td>\n",
-       "      <td>15.600000</td>\n",
-       "      <td>34.000000</td>\n",
-       "      <td>30.000000</td>\n",
-       "      <td>88.00000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>71922.500000</td>\n",
-       "      <td>18.500000</td>\n",
-       "      <td>38.000000</td>\n",
-       "      <td>33.000000</td>\n",
-       "      <td>95.00000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>320784.000000</td>\n",
-       "      <td>23.900000</td>\n",
-       "      <td>54.000000</td>\n",
-       "      <td>44.000000</td>\n",
-       "      <td>100.00000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       million_miles_annually  drvr_fatl_col_bmiles  perc_fatl_speed  \\\n",
-       "count               51.000000             51.000000        51.000000   \n",
-       "mean             57851.019608             15.790196        31.725490   \n",
-       "std              59898.414088              4.122002         9.633438   \n",
-       "min               3568.000000              5.900000        13.000000   \n",
-       "25%              17450.000000             12.750000        23.000000   \n",
-       "50%              46606.000000             15.600000        34.000000   \n",
-       "75%              71922.500000             18.500000        38.000000   \n",
-       "max             320784.000000             23.900000        54.000000   \n",
-       "\n",
-       "       perc_fatl_alcohol  perc_fatl_1st_time  \n",
-       "count          51.000000            51.00000  \n",
-       "mean           30.686275            88.72549  \n",
-       "std             5.132213             6.96011  \n",
-       "min            16.000000            76.00000  \n",
-       "25%            28.000000            83.50000  \n",
-       "50%            30.000000            88.00000  \n",
-       "75%            33.000000            95.00000  \n",
-       "max            44.000000           100.00000  "
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_total.describe() #text summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "9a2f49ad",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x1cf915f18b0>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 900x900 with 30 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.pairplot(df_total) #diagram summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "b9d64635",
-   "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>million_miles_annually</th>\n",
-       "      <th>drvr_fatl_col_bmiles</th>\n",
-       "      <th>perc_fatl_speed</th>\n",
-       "      <th>perc_fatl_alcohol</th>\n",
-       "      <th>perc_fatl_1st_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>million_miles_annually</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.077133</td>\n",
-       "      <td>-0.043199</td>\n",
-       "      <td>-0.034561</td>\n",
-       "      <td>-0.128928</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>drvr_fatl_col_bmiles</th>\n",
-       "      <td>-0.077133</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.029080</td>\n",
-       "      <td>0.199426</td>\n",
-       "      <td>-0.017942</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>perc_fatl_speed</th>\n",
-       "      <td>-0.043199</td>\n",
-       "      <td>-0.029080</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.286244</td>\n",
-       "      <td>0.014066</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>perc_fatl_alcohol</th>\n",
-       "      <td>-0.034561</td>\n",
-       "      <td>0.199426</td>\n",
-       "      <td>0.286244</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.245455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>perc_fatl_1st_time</th>\n",
-       "      <td>-0.128928</td>\n",
-       "      <td>-0.017942</td>\n",
-       "      <td>0.014066</td>\n",
-       "      <td>-0.245455</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                        million_miles_annually  drvr_fatl_col_bmiles  \\\n",
-       "million_miles_annually                1.000000             -0.077133   \n",
-       "drvr_fatl_col_bmiles                 -0.077133              1.000000   \n",
-       "perc_fatl_speed                      -0.043199             -0.029080   \n",
-       "perc_fatl_alcohol                    -0.034561              0.199426   \n",
-       "perc_fatl_1st_time                   -0.128928             -0.017942   \n",
-       "\n",
-       "                        perc_fatl_speed  perc_fatl_alcohol  perc_fatl_1st_time  \n",
-       "million_miles_annually        -0.043199          -0.034561           -0.128928  \n",
-       "drvr_fatl_col_bmiles          -0.029080           0.199426           -0.017942  \n",
-       "perc_fatl_speed                1.000000           0.286244            0.014066  \n",
-       "perc_fatl_alcohol              0.286244           1.000000           -0.245455  \n",
-       "perc_fatl_1st_time             0.014066          -0.245455            1.000000  "
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_total.corr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "b9c1ce8c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features = df_total.drop(['state','drvr_fatl_col_bmiles'], axis =1)\n",
-    "target =  df_total['drvr_fatl_col_bmiles']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "ca56b29b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "linear_reg = LinearRegression()\n",
-    "linear_reg.fit(features, target)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "f997f09c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-4.76649976e-06, -4.24831810e-02,  1.87339877e-01,  1.88197884e-02])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coef = linear_reg.coef_\n",
-    "coef"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "9140fb88",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scaler = StandardScaler()\n",
-    "df_scaled = scaler.fit_transform(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "b2f872ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PCA()"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pca = PCA()\n",
-    "pca.fit(df_scaled)\n",
-    "\n",
-    "\n",
-    "############## i will ask about this step"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "28c59003",
-   "metadata": {},
-   "outputs": [
-    {
-     "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=2)\n",
-    "pca_comps = pca.fit_transform(df_scaled)\n",
-    "\n",
-    "pca_comp1 = pca_comps[:,0]\n",
-    "pca_comp2 = pca_comps[:,1]\n",
-    "\n",
-    "plt.scatter(pca_comp1,pca_comp2)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "d63e2a20",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Eng.Mouradadel\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "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": [
-    "inertia = []\n",
-    "for k in range(1, 10):\n",
-    "    \n",
-    "    km = KMeans(n_clusters= k, random_state=8)\n",
-    "\n",
-    "    km.fit(df_scaled)\n",
-    "\n",
-    "    inertia.append(km.inertia_)\n",
-    "    \n",
-    "plt.plot(range(1, 10), inertia, marker='o')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "f65bae20",
-   "metadata": {},
-   "outputs": [
-    {
-     "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": [
-    "km = KMeans(n_clusters = 3, random_state=8)\n",
-    "km.fit(df_scaled)\n",
-    "\n",
-    "plt.scatter(pca_comps[:, 0], pca_comps[:, 1], c=km.labels_)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "76b9d7ef",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "eb18ea2e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# i will ask about this point"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "53dfd5f8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "8b6af0a5",
-   "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>million_miles_annually</th>\n",
-       "      <th>drvr_fatl_col_bmiles</th>\n",
-       "      <th>perc_fatl_speed</th>\n",
-       "      <th>perc_fatl_alcohol</th>\n",
-       "      <th>perc_fatl_1st_time</th>\n",
-       "      <th>num_drvr_fatl_col</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>64914</td>\n",
-       "      <td>18.8</td>\n",
-       "      <td>39</td>\n",
-       "      <td>30</td>\n",
-       "      <td>80</td>\n",
-       "      <td>1220.3832</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>4593</td>\n",
-       "      <td>18.1</td>\n",
-       "      <td>41</td>\n",
-       "      <td>25</td>\n",
-       "      <td>94</td>\n",
-       "      <td>83.1333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Arizona</td>\n",
-       "      <td>59575</td>\n",
-       "      <td>18.6</td>\n",
-       "      <td>35</td>\n",
-       "      <td>28</td>\n",
-       "      <td>96</td>\n",
-       "      <td>1108.0950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Arkansas</td>\n",
-       "      <td>32953</td>\n",
-       "      <td>22.4</td>\n",
-       "      <td>18</td>\n",
-       "      <td>26</td>\n",
-       "      <td>95</td>\n",
-       "      <td>738.1472</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>California</td>\n",
-       "      <td>320784</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>35</td>\n",
-       "      <td>28</td>\n",
-       "      <td>89</td>\n",
-       "      <td>3849.4080</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        state  million_miles_annually  drvr_fatl_col_bmiles  perc_fatl_speed  \\\n",
-       "0     Alabama                   64914                  18.8               39   \n",
-       "1      Alaska                    4593                  18.1               41   \n",
-       "2     Arizona                   59575                  18.6               35   \n",
-       "3    Arkansas                   32953                  22.4               18   \n",
-       "4  California                  320784                  12.0               35   \n",
-       "\n",
-       "   perc_fatl_alcohol  perc_fatl_1st_time  num_drvr_fatl_col  \n",
-       "0                 30                  80          1220.3832  \n",
-       "1                 25                  94            83.1333  \n",
-       "2                 28                  96          1108.0950  \n",
-       "3                 26                  95           738.1472  \n",
-       "4                 28                  89          3849.4080  "
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_total['num_drvr_fatl_col'] = (df_total['drvr_fatl_col_bmiles']/1000)*df_total['million_miles_annually']\n",
-    "df_total.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "ba49b735",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# i will ask about this point"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "78368852",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8b834fac",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 48595f3dac7a28a8be42858e3ba38ad1dc9e91f8 Mon Sep 17 00:00:00 2001
From: hirlekham28 <125371985+hirlekham28@users.noreply.github.com>
Date: Mon, 20 Feb 2023 19:17:22 +0530
Subject: [PATCH 2/3] Add files via upload

---
 Reducing traffic mortality.ipynb | 1840 ++++++++++++++++++++++++++++++
 1 file changed, 1840 insertions(+)
 create mode 100644 Reducing traffic mortality.ipynb

diff --git a/Reducing traffic mortality.ipynb b/Reducing traffic mortality.ipynb
new file mode 100644
index 0000000..c8c8f39
--- /dev/null
+++ b/Reducing traffic mortality.ipynb	
@@ -0,0 +1,1840 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a320aa1c",
+   "metadata": {},
+   "source": [
+    "## Read in and get an overview of the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "7ef57ea6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing the libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns \n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "acbb20ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load the dataset\n",
+    "df=pd.read_csv(\"road-accidents.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "2cc97aa5",
+   "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>##### LICENSE #####</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td># This data set is modified from the original ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td># and it is released under CC BY 4.0 (https://...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>##### COLUMN ABBREVIATIONS #####</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td># drvr_fatl_col_bmiles = Number of drivers inv...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td># perc_fatl_speed = Percentage Of Drivers Invo...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 ##### LICENSE #####\n",
+       "0  # This data set is modified from the original ...\n",
+       "1  # and it is released under CC BY 4.0 (https://...\n",
+       "2                   ##### COLUMN ABBREVIATIONS #####\n",
+       "3  # drvr_fatl_col_bmiles = Number of drivers inv...\n",
+       "4  # perc_fatl_speed = Percentage Of Drivers Invo..."
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "969b2760",
+   "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>drvr_fatl_col_bmiles</th>\n",
+       "      <th>perc_fatl_speed</th>\n",
+       "      <th>perc_fatl_alcohol</th>\n",
+       "      <th>perc_fatl_1st_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>18.8</td>\n",
+       "      <td>39</td>\n",
+       "      <td>30</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>18.1</td>\n",
+       "      <td>41</td>\n",
+       "      <td>25</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>35</td>\n",
+       "      <td>28</td>\n",
+       "      <td>96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>22.4</td>\n",
+       "      <td>18</td>\n",
+       "      <td>26</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>28</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>13.6</td>\n",
+       "      <td>37</td>\n",
+       "      <td>28</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>46</td>\n",
+       "      <td>36</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Delaware</td>\n",
+       "      <td>16.2</td>\n",
+       "      <td>38</td>\n",
+       "      <td>30</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>District of Columbia</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>34</td>\n",
+       "      <td>27</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Florida</td>\n",
+       "      <td>17.9</td>\n",
+       "      <td>21</td>\n",
+       "      <td>29</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  state  drvr_fatl_col_bmiles  perc_fatl_speed  \\\n",
+       "0               Alabama                  18.8               39   \n",
+       "1                Alaska                  18.1               41   \n",
+       "2               Arizona                  18.6               35   \n",
+       "3              Arkansas                  22.4               18   \n",
+       "4            California                  12.0               35   \n",
+       "5              Colorado                  13.6               37   \n",
+       "6           Connecticut                  10.8               46   \n",
+       "7              Delaware                  16.2               38   \n",
+       "8  District of Columbia                   5.9               34   \n",
+       "9               Florida                  17.9               21   \n",
+       "\n",
+       "   perc_fatl_alcohol  perc_fatl_1st_time  \n",
+       "0                 30                  80  \n",
+       "1                 25                  94  \n",
+       "2                 28                  96  \n",
+       "3                 26                  95  \n",
+       "4                 28                  89  \n",
+       "5                 28                  95  \n",
+       "6                 36                  82  \n",
+       "7                 30                  99  \n",
+       "8                 27                 100  \n",
+       "9                 29                  94  "
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1=pd.read_csv('road-accidents.csv', comment = '#', sep = '|')\n",
+    "df1.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "ee7b52eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 51 entries, 0 to 50\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   state                 51 non-null     object \n",
+      " 1   drvr_fatl_col_bmiles  51 non-null     float64\n",
+      " 2   perc_fatl_speed       51 non-null     int64  \n",
+      " 3   perc_fatl_alcohol     51 non-null     int64  \n",
+      " 4   perc_fatl_1st_time    51 non-null     int64  \n",
+      "dtypes: float64(1), int64(3), object(1)\n",
+      "memory usage: 2.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df1.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "75975bf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state                   0\n",
+       "drvr_fatl_col_bmiles    0\n",
+       "perc_fatl_speed         0\n",
+       "perc_fatl_alcohol       0\n",
+       "perc_fatl_1st_time      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "3d4cbf48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(51, 5)"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "e52b25d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     False\n",
+       "1     False\n",
+       "2     False\n",
+       "3     False\n",
+       "4     False\n",
+       "5     False\n",
+       "6     False\n",
+       "7     False\n",
+       "8     False\n",
+       "9     False\n",
+       "10    False\n",
+       "11    False\n",
+       "12    False\n",
+       "13    False\n",
+       "14    False\n",
+       "15    False\n",
+       "16    False\n",
+       "17    False\n",
+       "18    False\n",
+       "19    False\n",
+       "20    False\n",
+       "21    False\n",
+       "22    False\n",
+       "23    False\n",
+       "24    False\n",
+       "25    False\n",
+       "26    False\n",
+       "27    False\n",
+       "28    False\n",
+       "29    False\n",
+       "30    False\n",
+       "31    False\n",
+       "32    False\n",
+       "33    False\n",
+       "34    False\n",
+       "35    False\n",
+       "36    False\n",
+       "37    False\n",
+       "38    False\n",
+       "39    False\n",
+       "40    False\n",
+       "41    False\n",
+       "42    False\n",
+       "43    False\n",
+       "44    False\n",
+       "45    False\n",
+       "46    False\n",
+       "47    False\n",
+       "48    False\n",
+       "49    False\n",
+       "50    False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.duplicated()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7055de24",
+   "metadata": {},
+   "source": [
+    "## Create a textual and a graphical summary of the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "3d92396e",
+   "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>drvr_fatl_col_bmiles</th>\n",
+       "      <th>perc_fatl_speed</th>\n",
+       "      <th>perc_fatl_alcohol</th>\n",
+       "      <th>perc_fatl_1st_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>51.000000</td>\n",
+       "      <td>51.000000</td>\n",
+       "      <td>51.000000</td>\n",
+       "      <td>51.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>15.790196</td>\n",
+       "      <td>31.725490</td>\n",
+       "      <td>30.686275</td>\n",
+       "      <td>88.72549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>4.122002</td>\n",
+       "      <td>9.633438</td>\n",
+       "      <td>5.132213</td>\n",
+       "      <td>6.96011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>5.900000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>76.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>12.750000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>83.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>15.600000</td>\n",
+       "      <td>34.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>88.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>18.500000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>95.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>23.900000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>100.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       drvr_fatl_col_bmiles  perc_fatl_speed  perc_fatl_alcohol  \\\n",
+       "count             51.000000        51.000000          51.000000   \n",
+       "mean              15.790196        31.725490          30.686275   \n",
+       "std                4.122002         9.633438           5.132213   \n",
+       "min                5.900000        13.000000          16.000000   \n",
+       "25%               12.750000        23.000000          28.000000   \n",
+       "50%               15.600000        34.000000          30.000000   \n",
+       "75%               18.500000        38.000000          33.000000   \n",
+       "max               23.900000        54.000000          44.000000   \n",
+       "\n",
+       "       perc_fatl_1st_time  \n",
+       "count            51.00000  \n",
+       "mean             88.72549  \n",
+       "std               6.96011  \n",
+       "min              76.00000  \n",
+       "25%              83.50000  \n",
+       "50%              88.00000  \n",
+       "75%              95.00000  \n",
+       "max             100.00000  "
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "8675f202",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(df1.corr(), annot=True, cmap='coolwarm')\n",
+    "plt.title('Correlation Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "dff2f4bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x2008fce87c0>"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(df1.describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "1e5b77b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='drvr_fatl_col_bmiles', ylabel='state'>"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#no of drivers involved in fatal collisions per billion miles\n",
+    "plt.figure(figsize=(15,10))\n",
+    "\n",
+    "sns.barplot(x='drvr_fatl_col_bmiles', y='state',data=df1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "959d3243",
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "sns.scatterplot(x=df1['perc_fatl_alcohol'], y=df1['drvr_fatl_col_bmiles'])\n",
+    "plt.xlabel('Percentage of fatal accidents involving alcohol')\n",
+    "plt.ylabel('Number of drivers involved in fatal accidents per billion miles')\n",
+    "plt.title('Relationship between alcohol-related accidents and driver fatalities')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "11771c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "sns.scatterplot(x=df1['perc_fatl_speed'], y=df1['drvr_fatl_col_bmiles'])\n",
+    "plt.xlabel('Percentage of fatal accidents involving speed')\n",
+    "plt.ylabel('Number of drivers involved in fatal accidents per billion miles')\n",
+    "plt.title('Relationship between speed-related accidents and driver fatalities')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b951f785",
+   "metadata": {},
+   "source": [
+    "## Quantify the association of features and accidents\n",
+    "The Pearson correlation coefficient is one of the most common methods to quantify correlation between variables, and by convention, the following thresholds are usually used:\n",
+    "\n",
+    "0.2 = weak\n",
+    "0.5 = medium\n",
+    "0.8 = strong\n",
+    "0.9 = very strong\n",
+    "\n",
+    "they are only highly correlated to their own features and very less correlated to other features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "c3356c7c",
+   "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>drvr_fatl_col_bmiles</th>\n",
+       "      <th>perc_fatl_speed</th>\n",
+       "      <th>perc_fatl_alcohol</th>\n",
+       "      <th>perc_fatl_1st_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>drvr_fatl_col_bmiles</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.029080</td>\n",
+       "      <td>0.199426</td>\n",
+       "      <td>-0.017942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>perc_fatl_speed</th>\n",
+       "      <td>-0.029080</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.286244</td>\n",
+       "      <td>0.014066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>perc_fatl_alcohol</th>\n",
+       "      <td>0.199426</td>\n",
+       "      <td>0.286244</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.245455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>perc_fatl_1st_time</th>\n",
+       "      <td>-0.017942</td>\n",
+       "      <td>0.014066</td>\n",
+       "      <td>-0.245455</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      drvr_fatl_col_bmiles  perc_fatl_speed  \\\n",
+       "drvr_fatl_col_bmiles              1.000000        -0.029080   \n",
+       "perc_fatl_speed                  -0.029080         1.000000   \n",
+       "perc_fatl_alcohol                 0.199426         0.286244   \n",
+       "perc_fatl_1st_time               -0.017942         0.014066   \n",
+       "\n",
+       "                      perc_fatl_alcohol  perc_fatl_1st_time  \n",
+       "drvr_fatl_col_bmiles           0.199426           -0.017942  \n",
+       "perc_fatl_speed                0.286244            0.014066  \n",
+       "perc_fatl_alcohol              1.000000           -0.245455  \n",
+       "perc_fatl_1st_time            -0.245455            1.000000  "
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf69fa67",
+   "metadata": {},
+   "source": [
+    "## Fit a multivariate linear regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "f30466f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=df1[['perc_fatl_1st_time','perc_fatl_speed','perc_fatl_alcohol']]\n",
+    "y=df1['drvr_fatl_col_bmiles'] \n",
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=1/3,random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "c87317bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "regr = linear_model.LinearRegression()\n",
+    "regr.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "d8d84333",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intercept: \n",
+      " 17.580811749575172\n",
+      "Coefficients: \n",
+      " [-0.01650619 -0.03433368  0.02298113]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Intercept: \\n', regr.intercept_)\n",
+    "print('Coefficients: \\n', regr.coef_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "faea03cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.009703741818558154"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred=regr.predict(x_test)\n",
+    "y_pred\n",
+    "r_sq=regr.score(x,y)\n",
+    "r_sq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "8e661025",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error: 10.396297495626518\n"
+     ]
+    }
+   ],
+   "source": [
+    "mse = np.mean((y_pred - y_test)**2)\n",
+    "print(\"Mean Squared Error:\", mse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "2d72682c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Absolute Error: 2.6659414168903925\n"
+     ]
+    }
+   ],
+   "source": [
+    "mae = np.mean(np.abs(y_pred - y_test))\n",
+    "print(\"Mean Absolute Error:\", mae)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f42dcd7",
+   "metadata": {},
+   "source": [
+    "## Perform PCA on standardized data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "ac5b4d5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.26611685,  0.76264511, -0.1350496 ],\n",
+       "       [ 0.76536053,  0.97232113, -1.1189824 ],\n",
+       "       [ 1.05557158,  0.34329308, -0.52862272],\n",
+       "       [ 0.91046605, -1.43895304, -0.92219584],\n",
+       "       [ 0.03983289,  0.34329308, -0.52862272],\n",
+       "       [ 0.91046605,  0.5529691 , -0.52862272],\n",
+       "       [-0.9759058 ,  1.49651116,  1.04566976],\n",
+       "       [ 1.49088816,  0.6578071 , -0.1350496 ],\n",
+       "       [ 1.63599369,  0.23845508, -0.72540928],\n",
+       "       [ 0.76536053, -1.12443902, -0.33183616],\n",
+       "       [ 0.620255  , -1.33411503, -1.1189824 ],\n",
+       "       [-0.25037816,  2.33521522,  2.02960256],\n",
+       "       [ 1.34578263,  0.44813109, -0.33183616],\n",
+       "       [ 1.05557158,  0.44813109,  0.65209664],\n",
+       "       [ 0.91046605, -0.70508699, -0.33183616],\n",
+       "       [-0.25037816, -1.54379105, -1.1189824 ],\n",
+       "       [-0.54058922, -0.49541097, -1.31576896],\n",
+       "       [-1.84653896, -1.33411503, -1.51255552],\n",
+       "       [ 1.34578263,  0.34329308,  0.45531008],\n",
+       "       [-0.68569475,  0.6578071 , -0.1350496 ],\n",
+       "       [ 1.49088816,  0.23845508,  0.25852352],\n",
+       "       [-1.26611685, -0.914763  ,  0.8488832 ],\n",
+       "       [-1.70143344, -0.809925  , -0.52862272],\n",
+       "       [-0.10527264, -0.914763  , -0.33183616],\n",
+       "       [ 1.63599369, -1.75346706,  0.06173696],\n",
+       "       [-0.68569475,  1.18199714,  0.65209664],\n",
+       "       [-0.54058922,  0.76264511,  2.61996224],\n",
+       "       [ 0.18493842, -1.96314307,  0.8488832 ],\n",
+       "       [ 1.49088816,  0.5529691 ,  0.25852352],\n",
+       "       [-0.83080027,  0.34329308, -0.1350496 ],\n",
+       "       [-1.55632791, -1.64862905, -0.52862272],\n",
+       "       [ 1.34578263, -1.33411503, -0.72540928],\n",
+       "       [-1.26611685,  0.02877906, -0.33183616],\n",
+       "       [-1.12101133,  0.76264511,  0.06173696],\n",
+       "       [-0.39548369, -0.914763  ,  2.22638912],\n",
+       "       [-0.9759058 , -0.39057297,  0.65209664],\n",
+       "       [ 0.76536053,  0.02877906, -0.33183616],\n",
+       "       [ 0.18493842,  0.13361707, -0.92219584],\n",
+       "       [-0.10527264,  1.91586319,  0.06173696],\n",
+       "       [-1.41122238,  0.23845508,  1.43924288],\n",
+       "       [-1.12101133,  0.6578071 ,  2.02960256],\n",
+       "       [-0.39548369, -0.07605895,  0.45531008],\n",
+       "       [-1.12101133, -1.12443902, -0.33183616],\n",
+       "       [-0.25037816,  0.86748312,  1.43924288],\n",
+       "       [ 1.05557158,  1.18199714, -2.89006144],\n",
+       "       [ 0.91046605, -0.18089695, -0.1350496 ],\n",
+       "       [-0.10527264, -1.33411503, -0.72540928],\n",
+       "       [-0.39548369,  1.07715913,  0.45531008],\n",
+       "       [-0.25037816,  0.23845508, -0.52862272],\n",
+       "       [-0.68569475,  0.44813109,  0.45531008],\n",
+       "       [ 0.18493842,  1.07715913,  0.25852352]])"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#standardising the features\n",
+    "scaler = StandardScaler()\n",
+    "features_scaled = scaler.fit_transform(x)\n",
+    "features_scaled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "3376c48a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.88463912,  0.46421102,  1.09765186],\n",
+       "       [-0.62529903, -1.22024698,  0.95042366],\n",
+       "       [-0.67169596, -1.02786253,  0.06202125],\n",
+       "       [-1.83954608,  0.23991011, -0.5554213 ],\n",
+       "       [-0.21174283, -0.25666671,  0.53681091],\n",
+       "       [-0.49390467, -1.05414432,  0.24290566],\n",
+       "       [ 1.98805561, -0.22697834,  0.53054572],\n",
+       "       [-0.41984695, -1.56080846, -0.24759157],\n",
+       "       [-1.13099129, -1.40144229, -0.12795702],\n",
+       "       [-1.18444358,  0.14877025, -0.73158452],\n",
+       "       [-1.79251216,  0.39090271, -0.2253797 ],\n",
+       "       [ 2.80996806, -1.31802924, -0.04565428],\n",
+       "       [-0.60664618, -1.31530775, -0.15496303],\n",
+       "       [ 0.2268846 , -1.08935235, -0.70860264],\n",
+       "       [-1.02598376, -0.23430583, -0.57329698],\n",
+       "       [-1.51035032,  1.18838032,  0.06852555],\n",
+       "       [-0.95893975,  0.72533611,  0.90732506],\n",
+       "       [-0.95632936,  2.26156134,  1.2033984 ],\n",
+       "       [-0.10099555, -1.24259045, -0.76292673],\n",
+       "       [ 0.56576691,  0.091754  ,  0.76981483],\n",
+       "       [-0.36316811, -1.28565771, -0.74942372],\n",
+       "       [ 0.6900851 ,  1.56144575, -0.49610044],\n",
+       "       [-0.03971211,  1.81587269,  0.7289208 ],\n",
+       "       [-0.67811433,  0.67334261, -0.21156464],\n",
+       "       [-1.63409399, -0.10065137, -1.75343653],\n",
+       "       [ 1.40766864, -0.24488656,  0.50102308],\n",
+       "       [ 2.52202483, -0.07092466, -1.17150623],\n",
+       "       [-0.52740931,  1.14200056, -1.73965795],\n",
+       "       [-0.19504256, -1.49033665, -0.57983775],\n",
+       "       [ 0.46334895,  0.40660377,  0.66805595],\n",
+       "       [-0.5537545 ,  2.25151236,  0.20886445],\n",
+       "       [-1.84020388, -0.15770595, -0.84023269],\n",
+       "       [ 0.35192305,  0.94067245,  0.83981002],\n",
+       "       [ 0.95935465,  0.35516293,  0.89196601],\n",
+       "       [ 1.27880139,  0.90828   , -1.86807431],\n",
+       "       [ 0.69845606,  0.99884978, -0.21125259],\n",
+       "       [-0.56798322, -0.60171918, -0.10976929],\n",
+       "       [-0.67038036, -0.23263042,  0.63164402],\n",
+       "       [ 1.11586188, -1.16652231,  1.03899158],\n",
+       "       [ 1.79352241,  0.9244954 , -0.2200344 ],\n",
+       "       [ 2.30754399,  0.43461673, -0.54315023],\n",
+       "       [ 0.46332813,  0.35236477, -0.17511623],\n",
+       "       [-0.3302449 ,  1.58099105,  0.1501677 ],\n",
+       "       [ 1.60411279, -0.36622912, -0.42347921],\n",
+       "       [-1.90843858, -1.58714601,  2.16855561],\n",
+       "       [-0.60535139, -0.57431464, -0.42851245],\n",
+       "       [-1.18312797,  0.94400236, -0.16196175],\n",
+       "       [ 1.07978849, -0.39812466,  0.44669899],\n",
+       "       [-0.1363695 ,  0.03190126,  0.61593644],\n",
+       "       [ 0.87495256,  0.23157487,  0.24318124],\n",
+       "       [ 0.67653501, -0.83993073,  0.31324938]])"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Perform PCA on standardized data\n",
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA()\n",
+    "x_pca = pca.fit_transform(features_scaled)\n",
+    "x_pca\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "82381b47",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAK8ElEQVR4nO3cb4hd+V3H8ffHSYNSlYIZcEmiEzS4BKm1jKmgKIoLye6DbFEwVVqsLSEPou0DoXkkSJ/sPhERoiFoEEEMwlYJ3ZFQpFKkrWa2rIvZbcoQVzJmy07/aA2KadZvH8xVrtM7c89kZ3J3vnm/4MI95/y49wsH3hzO3DOpKiRJe993zHoASdLOMOiS1IRBl6QmDLokNWHQJamJfbP64gMHDtTCwsKsvl6S9qQXXnjhK1U1P+nYzIK+sLDA8vLyrL5ekvakJP+y2TFvuUhSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITM3tS9M1YOP/8rEdo69Vnnpr1CJIekFfoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNDAp6khNJbiZZSXJ+i3U/keSNJL+0cyNKkoaYGvQkc8AF4CRwDHhfkmObrHsWuLbTQ0qSphtyhX4cWKmqW1V1D7gCnJqw7jeA54DXd3A+SdJAQ4J+ELg9tr062vd/khwE3gtc3OqDkpxJspxkeW1tbbuzSpK2MCTombCvNmz/HvCxqnpjqw+qqktVtVhVi/Pz8wNHlCQNsW/AmlXg8Nj2IeDOhjWLwJUkAAeAJ5Pcr6q/2okhJUnTDQn6deBokiPAvwKngV8ZX1BVR/73fZI/AT5pzCXp4Zoa9Kq6n+Qc679emQMuV9WNJGdHx7e8by5JejiGXKFTVUvA0oZ9E0NeVb/25seSJG2XT4pKUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU3sm/UAejQsnH9+1iO09eozT816BL1FeIUuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUxKCgJzmR5GaSlSTnJxw/leSlJC8mWU7y0zs/qiRpK1P/fW6SOeAC8ASwClxPcrWqXh5b9jfA1aqqJO8E/gJ4fDcGliRNNuQK/TiwUlW3quoecAU4Nb6gqu5WVY023w4UkqSHakjQDwK3x7ZXR/v+nyTvTfJF4Hng1yd9UJIzo1syy2traw8yryRpE0OCngn7vu0KvKr+sqoeB54GPj7pg6rqUlUtVtXi/Pz8tgaVJG1tSNBXgcNj24eAO5strqrPAD+U5MCbnE2StA1Dgn4dOJrkSJL9wGng6viCJD+cJKP37wb2A1/d6WElSZub+iuXqrqf5BxwDZgDLlfVjSRnR8cvAr8IfCDJN4H/An557I+kkqSHYGrQAapqCVjasO/i2PtngWd3djRJ0nb4pKgkNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJamJQ0JOcSHIzyUqS8xOO/2qSl0avzyb5sZ0fVZK0lX3TFiSZAy4ATwCrwPUkV6vq5bFl/wz8bFV9PclJ4BLwnt0YWNLDsXD++VmP0Narzzy1K5875Ar9OLBSVbeq6h5wBTg1vqCqPltVXx9tfh44tLNjSpKmGRL0g8Dtse3V0b7NfAj460kHkpxJspxkeW1tbfiUkqSphgQ9E/bVxIXJz7Ee9I9NOl5Vl6pqsaoW5+fnh08pSZpq6j101q/ID49tHwLubFyU5J3AHwEnq+qrOzOeJGmoIVfo14GjSY4k2Q+cBq6OL0jyA8AngPdX1Zd2fkxJ0jRTr9Cr6n6Sc8A1YA64XFU3kpwdHb8I/DbwfcAfJAG4X1WLuze2JGmjIbdcqKolYGnDvotj7z8MfHhnR5MkbYdPikpSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgYFPcmJJDeTrCQ5P+H440k+l+S/k/zWzo8pSZpm37QFSeaAC8ATwCpwPcnVqnp5bNnXgN8Ent6NISVJ0w25Qj8OrFTVraq6B1wBTo0vqKrXq+o68M1dmFGSNMCQoB8Ebo9tr472bVuSM0mWkyyvra09yEdIkjYxJOiZsK8e5Muq6lJVLVbV4vz8/IN8hCRpE0OCvgocHts+BNzZnXEkSQ9qSNCvA0eTHEmyHzgNXN3dsSRJ2zX1Vy5VdT/JOeAaMAdcrqobSc6Ojl9M8v3AMvC9wP8k+ShwrKq+sXujS5LGTQ06QFUtAUsb9l0ce/9l1m/FSJJmxCdFJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmhgU9CQnktxMspLk/ITjSfL7o+MvJXn3zo8qSdrK1KAnmQMuACeBY8D7khzbsOwkcHT0OgP84Q7PKUmaYsgV+nFgpapuVdU94ApwasOaU8Cf1rrPA+9I8tgOzypJ2sK+AWsOArfHtleB9wxYcxB4bXxRkjOsX8ED3E1yc1vT7l0HgK/Meogh8uysJ3jL8JztLXvmfMGbPmc/uNmBIUHPhH31AGuoqkvApQHf2UqS5apanPUcGs5ztrd4vtYNueWyChwe2z4E3HmANZKkXTQk6NeBo0mOJNkPnAaublhzFfjA6NcuPwn8e1W9tvGDJEm7Z+otl6q6n+QccA2YAy5X1Y0kZ0fHLwJLwJPACvCfwAd3b+Q96ZG7zdSA52xv8XwBqfq2W92SpD3IJ0UlqQmDLklNGPRdlORykteT/NOsZ9F0SQ4n+XSSV5LcSPKRWc+krSX5ziT/kOQfR+fsd2Y90yx5D30XJfkZ4C7rT9H+6Kzn0dZGTzc/VlVfSPI9wAvA01X18oxH0yaSBHh7Vd1N8jbg74CPjJ5Yf+R4hb6LquozwNdmPYeGqarXquoLo/f/AbzC+hPPeosa/buRu6PNt41ej+xVqkGXJkiyAPw48PczHkVTJJlL8iLwOvCpqnpkz5lBlzZI8t3Ac8BHq+obs55HW6uqN6rqXaw/oX48ySN7e9OgS2NG92GfA/6sqj4x63k0XFX9G/C3wInZTjI7Bl0aGf2B7Y+BV6rqd2c9j6ZLMp/kHaP33wX8AvDFmQ41QwZ9FyX5c+BzwI8kWU3yoVnPpC39FPB+4OeTvDh6PTnrobSlx4BPJ3mJ9f879amq+uSMZ5oZf7YoSU14hS5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ18S0jfGTL9D1eTgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca=PCA()\n",
+    "pca.fit(features_scaled)\n",
+    "plt.bar(range(1,pca.n_components_+1),pca.explained_variance_ratio_)\n",
+    "plt.xticks([1,2,3])\n",
+    "plt.show()\n",
+    "     "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ed4849c",
+   "metadata": {},
+   "source": [
+    "## Visualize the first two principal components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "id": "c0587a1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.88463912,  0.46421102],\n",
+       "       [-0.62529903, -1.22024698],\n",
+       "       [-0.67169596, -1.02786253],\n",
+       "       [-1.83954608,  0.23991011],\n",
+       "       [-0.21174283, -0.25666671],\n",
+       "       [-0.49390467, -1.05414432],\n",
+       "       [ 1.98805561, -0.22697834],\n",
+       "       [-0.41984695, -1.56080846],\n",
+       "       [-1.13099129, -1.40144229],\n",
+       "       [-1.18444358,  0.14877025],\n",
+       "       [-1.79251216,  0.39090271],\n",
+       "       [ 2.80996806, -1.31802924],\n",
+       "       [-0.60664618, -1.31530775],\n",
+       "       [ 0.2268846 , -1.08935235],\n",
+       "       [-1.02598376, -0.23430583],\n",
+       "       [-1.51035032,  1.18838032],\n",
+       "       [-0.95893975,  0.72533611],\n",
+       "       [-0.95632936,  2.26156134],\n",
+       "       [-0.10099555, -1.24259045],\n",
+       "       [ 0.56576691,  0.091754  ],\n",
+       "       [-0.36316811, -1.28565771],\n",
+       "       [ 0.6900851 ,  1.56144575],\n",
+       "       [-0.03971211,  1.81587269],\n",
+       "       [-0.67811433,  0.67334261],\n",
+       "       [-1.63409399, -0.10065137],\n",
+       "       [ 1.40766864, -0.24488656],\n",
+       "       [ 2.52202483, -0.07092466],\n",
+       "       [-0.52740931,  1.14200056],\n",
+       "       [-0.19504256, -1.49033665],\n",
+       "       [ 0.46334895,  0.40660377],\n",
+       "       [-0.5537545 ,  2.25151236],\n",
+       "       [-1.84020388, -0.15770595],\n",
+       "       [ 0.35192305,  0.94067245],\n",
+       "       [ 0.95935465,  0.35516293],\n",
+       "       [ 1.27880139,  0.90828   ],\n",
+       "       [ 0.69845606,  0.99884978],\n",
+       "       [-0.56798322, -0.60171918],\n",
+       "       [-0.67038036, -0.23263042],\n",
+       "       [ 1.11586188, -1.16652231],\n",
+       "       [ 1.79352241,  0.9244954 ],\n",
+       "       [ 2.30754399,  0.43461673],\n",
+       "       [ 0.46332813,  0.35236477],\n",
+       "       [-0.3302449 ,  1.58099105],\n",
+       "       [ 1.60411279, -0.36622912],\n",
+       "       [-1.90843858, -1.58714601],\n",
+       "       [-0.60535139, -0.57431464],\n",
+       "       [-1.18312797,  0.94400236],\n",
+       "       [ 1.07978849, -0.39812466],\n",
+       "       [-0.1363695 ,  0.03190126],\n",
+       "       [ 0.87495256,  0.23157487],\n",
+       "       [ 0.67653501, -0.83993073]])"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca = PCA(n_components = 2)\n",
+    "pca2 = pca.fit_transform(x_pca)\n",
+    "pca2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "3f3e4f70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x20091130c40>"
+      ]
+     },
+     "execution_count": 183,
+     "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_1 = pca2[:,0]\n",
+    "pca_2 = pca2[:,1]\n",
+    "\n",
+    "plt.scatter(pca_1, pca_2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a85d0660",
+   "metadata": {},
+   "source": [
+    "## Find clusters of similar states in the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "c3853ddf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HIRLEKHA\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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B04DxwGTgHjNLD8e5F5gBjAqvye0Qe5upKCuh3tF83yLSYSU0YZhZHnAx8ACAux9y911ABTAzbDYTmBqWK4BH3b3W3dcAq4BJZlYE5Ln7Gx4N//pQzD4dwuj+uZwxIFfNUiLSYSW6hjEcqAZ+aWbvmNn9ZpYN9Hf3zQDhvTBsXwJsiNl/YygrCcuNy09gZjPMrNLMKqurq9v2ak7TlLJi3l6/S/N9i0iHlOiEkQGcA9zr7mcD+wjNTyfR1H0Jb6b8xEL3+9y93N3LCwoKWhtvQl17VjTf95xFeiZDRDqeRCeMjcBGd38rfH6MKIFsDc1MhPdtMdsPitl/IFAVygc2Ud6hDOqbRfmQPhryXEQ6pIQmDHffAmwwszGh6DLgPWAOMD2UTQdmh+U5wDQz62Fmw4hubs8PzVY1ZnZe6B11Q8w+HcqUsmJWbK1h+ZY9yQ5FRKRV2qOX1JeBh83sXaAM+AFwJ3CFma0ErgifcfelwCyipPIMcLO7N4zadxNwP9GN8A+Ap9sh9jZ3dL5v1TJEpIOxzjzndHl5uVdWViY7jBNM/8V8Vm3by2tf/xgd6HESEekCzGyBu5c3tU5PeifBlNJiNu06wNvrP0x2KCIicVPCSIJPjO9PD833LSIdjBJGEuRmduPysf156t3Nmu9bRDoMJYwkuba0mB37DvH6B5rvW0Q6BiWMJLl0TAG5mRlqlhKRDkMJI0kyu6XzyQkDeHap5vsWkY5BCSOJppSWsLf2CH9arvm+RST1KWEk0fkjovm+1SwlIh2BEkYSpacZ15YW8acV29h9QPN9i0hqU8JIsiml0Xzfzy7dkuxQRESaFXfCMLNfxVMmrVM2qDeD+2bxpIY8F5EU15oaxvjYD2Hq1HPbNpyux8yoKIvm+95WczDZ4YiInFSLCcPM7jCzGuAsM9sTXjVEc1h0yCHGU82U0mLqHf74rub7FpHU1WLCcPf/cPdc4Efunhdeue7ez93vaIcYO71R/XMZW5SnmfhEJKW1pknqqTAfN2Z2vZndZWZDEhRXl1NRVsw763exfofm+xaR1NSahHEvsN/MSoGvAeuAhxISVRd0bWnDfN+bkhyJiEjTWpMwjng021IF8FN3/ymQm5iwup6S3j2ZOLQPsxdW0ZkntRKRjqs1CaPGzO4Argf+GHpJdUtMWF3TlLISVm7by/ItNckORUTkBK1JGJ8FaoEb3X0LUAL8qKWdzGytmS02s4VmVhnK+prZ82a2Mrz3idn+DjNbZWYrzOzKmPJzw3FWmdnd1gnnNr1qwgDS04zZGipERFJQ3AnD3be4+13u/mr4vN7d472H8TF3L4uZJ/Z2YJ67jwLmhc+Y2ThgGtEzH5OBe0JNBqJ7KDOAUeE1Od7YO4p+OT24aFQ+Ty6qor5ezVIiklrieQ7jtfBeE/Mcxp6Gz6d43gpgZlieCUyNKX/U3WvdfQ2wCphkZkVAnru/Ee6jPBSzT6dSUab5vkUkNcXzHMZHw3tuzHMYDc9i5MVxDgeeM7MFZjYjlPV3983huJuBwlBeAmyI2XdjKCsJy43LT2BmM8ys0swqq6ur4wgvtVwxbgA9MtLULCUiKSeeGkbf5l5xnONCdz8H+CRws5ld3NzpmijzZspPLHS/z93L3b28oKAgjvBSS06PDC4f15+5izdzWPN9i0gKyYhjmwU0/6M9vLmd3b0qvG8zsz8Ak4CtZlbk7ptDc1PDDEIbgUExuw8EqkL5wCbKO6WK0mL++O5mXl+1nUvHFLa8g4hIO4inSWqYuw8P741fR5OFmY1vvK+ZZZtZbsMy8AlgCTAHmB42m86xManmANPMrIeZDSO6uT0/NFvVmNl5oXfUDXTicawuGVNAnub7FpEUE08NI16/As5pVNYf+EPoAZsB/MbdnzGzvwCzzOxGYD3waQB3X2pms4D3gCPAze7eMOH1TcCDQE/g6fDqlHpkpPPJCUU89W4VBw/XkdktveWdREQSrC0TxglNVu6+GihtonwHcFlTB3H37wPfb6K8Ephw+mF2DBVlxfy2cgOzKjdww/lDkx2OiEibzrinBwfa0HnD+/HRkfn8+1Pv8Ze1O5MdjoiIpmhNVWlpxs8/fw6D+mTx979awIadGsVWRJKrLRPGoTY8lgC9srpx//RyjtTV87czK9lbeyTZIYlIF9aaOb0vbG4+DHc/LxEBdnXDC3K457pzWVW9l1sfeYc6DRkiIkmi+TA6gI+Oyuc7U8Yzb/k2fvjM8mSHIyJdlObD6CD++rwh3HD+EO57ZTWzKje0vIOISBtrTbfa2PkwLtZ8GO3vX64Zx+rqfXzzD4sZ2i+bScPiGZlFRKRtJHw+DGk7GelpR3tO/cOv1XNKRNpXq2bcI2qKetXMRgNlwCMJiUpOqldWNx74m4nU1Ts3zvwLNQcPJzskEekiWpMwXgF6mFkJ0aRHXyAaqkPa2bD8bO697hw+qN7HrY8uVM8pEWkXrUkY5u77gU8BP3P3/49oZjxJggtG5vPdKeP50/Jt3Pn0smSHIyJdQGtuepuZnQ9cB9wYyjQqXhJdf94QVm6t4X9fXcOowlw+M3FQyzuJiJyi1tQwbgPuAP4QRpUdDryYkKgkbt++ZhwXjcrnm08s5q3VO5Idjoh0YnEnDHd/2d2nuPsPw+fV7n5L4kKTeGSkp/Ffnz+HQX2jnlPrd6jnlIgkRmuGBnnezHrHfO5jZs8mJCpplV49u/HA9InUO+o5JSIJ05omqQJ339Xwwd0/JJogSVJAQ8+pNdv3cYvGnBKRBGhNwqgzs8ENH8LAg/VtH5KcqgtG5vPdivG8uKKa/5irnlMi0rZa00vqG8CrZvZK+HwxMKPtQ5LTcd1HhrBy617uf20No/rn8NmJg1veSUQkDq2pYVwP/ByYD8wCznX3uO5hmFm6mb1jZk+Fz33DPZGV4b1PzLZ3mNkqM1thZlfGlJ9rZovDurstTBQuJ/rW1WO5aFQ+33piiXpOiUibaU3C+CWQCUwBfgL8j5ndGue+twKxbSS3A/PcfRTRU+O3A5jZOGAa0QOBk4F7wiCHEA2vPgMYFV6TWxF7l6KeUyKSCK3pVvsn4PvAt4H7gXLgppb2M7OBwNVhnwYVwMywPBOYGlP+qLvXuvsaYBUwycyKgDx3fyMMsf5QzD7ShF49u/EL9ZwSkTbUmm6184DXiUatXQFMdPcz4tj1J0QTLsXeIO/v7psBwnthKC8BYid72BjKSsJy4/Km4pxhZpVmVlldXR1HeJ3X0Pxs7r0+6jn1ZfWcEpHT1JomqXeJ5u2eAJwFTDCzns3tYGbXANvcfUGc52jqvoQ3U35ioft97l7u7uUFBQVxnrbzumBE1HPqpRXV/EA9p0TkNMTdS8rdvwJgZjlEI9X+EhgA9GhmtwuBKWZ2FdH9jzwz+zWw1cyK3H1zaG7aFrbfCMQOiDQQqArlA5solzg09Jx64LU1jCrMYdok9ZwSkdZrTZPUl8zst8BCovsHvwA+2dw+7n6Huw9096FEN7P/5O7XA3OA6WGz6cDssDwHmGZmPcxsGNHN7fmh2arGzM4LvaNuiNlH4vCtq8dy8egCvvXEEt5UzykROQWtaZLqCdwFnOHul7n7d8ON8FNxJ3CFma0ErgifcfelRF123wOeAW5297qwz01EN85XAR8AT5/iubukqOfU2Qzpl8VNv17Auh37kh2SiHQwFnU66pzKy8u9srIy2WGklLXb9zH1ntfJz+nB4/94AXmZmpZdRI4xswXuXt7UutbUMKQTGJqfzb3Xncva7fv48m/e4UidRncRkfgoYXRB54/ox79PncDL71fzg7nLkx2OiHQQrRlLSjqRz00azPtba/jF69GYU59TzykRaYFqGF3YN68ayyWjC/j2E0t44wP1nBKR5ilhdGEZ6Wn87PNnMzQ/m5seVs8pEWmeEkYXl5fZjQemRx0ibpxZyR6NOSUiJ6GEIQzpd6zn1JfUc0pETkIJQ4Co59T3pk7glfer+b7GnBKRJqiXlBw1bdJg3t+6N+o5VZjL5z+inlMicoxqGHKcb1x1BpeOKeBfZi/hzx9sT3Y4IpJClDDkOBnpadz9ubMZlp/NPz78Nmu3q+eUiESUMOQEUc+piRjRbH27D6jnlIgoYchJDO6XxX9ffy7rd+7ny4+8w8HDdS3vJCKdmhKGnNRHhvfj+1PP5JX3q5n8k1f48yrd0xDpypQwpFmfmTiI3/ztRwD4/P1v8c+zFrFz36EkRyUiyaCEIS26YGQ+z9x2MV/62EhmL9zEZf/5Eo+/vZHOPJeKiJxICUPiktktna9eOYa5t17E8IIc/mnWIq5/4C31ohLpQpQwpFVG98/ld39/Pt+bOoF3N+zmyp+8ws9fXMVhDSci0uklNGGYWaaZzTezRWa21My+G8r7mtnzZrYyvPeJ2ecOM1tlZivM7MqY8nPNbHFYd7eZWSJjl5NLSzOuP28IL/zzJVw2tpAfPbuCa+5+jQXrPkx2aCKSQImuYdQCH3f3UqAMmGxm5wG3A/PcfRQwL3zGzMYB04DxwGTgHjNLD8e6F5gBjAqvyQmOXVrQPy+Te647l/tvKKfm4GH+z3//mW8/sUQj3op0UglNGB7ZGz52Cy8HKoCZoXwmMDUsVwCPunutu68BVgGTzKwIyHP3Nzy60/pQzD6SZJeP68/z/3QJX7hgGA+/tY4r7nqZZ5Zs1k1xkU4m4fcwzCzdzBYC24Dn3f0toL+7bwYI74Vh8xJgQ8zuG0NZSVhuXN7U+WaYWaWZVVZXV7fptcjJZffI4F+uHccTN19Iv+we/MOv3+bvHlpA1a4DyQ5NRNpIwhOGu9e5exkwkKi2MKGZzZu6L+HNlDd1vvvcvdzdywsKClodr5yeswb2Zs6XLuSbV43l9VXbueKul/nl62uoq1dtQ6Sja7deUu6+C3iJ6N7D1tDMRHjfFjbbCAyK2W0gUBXKBzZRLikoIz2Nv7t4OM995WImDuvLd598j0/d8zrvVe1JdmgichoS3UuqwMx6h+WewOXAcmAOMD1sNh2YHZbnANPMrIeZDSO6uT0/NFvVmNl5oXfUDTH7SIoa1DeLX/7NRH72ubPZtOsA1/7Xa/zH3GUcOKRxqUQ6okRPoFQEzAw9ndKAWe7+lJm9AcwysxuB9cCnAdx9qZnNAt4DjgA3u3vDr8tNwINAT+Dp8JIUZ2ZcW1rMxaMKuPOZZfzPK6uZu2Qz35t6JpeMVpOhSEdinbknS3l5uVdWViY7DIkxf81O7nj8XT6o3kdFWTHfvmYc+Tk9kh2WiARmtsDdy5tapye9pV1NGtaXubdexG2Xj+LpxVu47D9f5rd/Wa8uuCIdgBKGtLseGencdvlo5t56EWMG5PL13y9m2n1v8kH13pZ3FpGkUcKQpBlZmMOjf3ceP/yrM1m+pYZP/uRVfvrCSmqP6Ka4SCpSwpCkSkszPjtxMC/80yVMnjCA//+F97nqp68yf83OZIcmIo0oYUhKKMjtwd2fO5sHvzCR2iP1fOZ/3uCOx99l936NSyWSKpQwJKVcOqaQ575yMTMuHs6syo1cdtfLPLmoSjfFRVKAEoaknKzuGXzjqrHM+dKFFPfO5MuPvMMXHvwLG3buT3ZoIl2ansOQlFZX78z881p+/NwKDh2p5+LRBVSUFXP52P5k90j0c6ciXU9zz2HoX5yktPQ044sfHcbkCQOY+ee1PLmoij8t30ZmtzQuH9ufKaXFXDKmgB4Z6S0fTEROi2oY0qHU1zuV6z5kzqJNzF28hZ37DpGXmcEnJxRRUVbMR4b3Iz1NkzGKnKrmahhKGNJhHa6r5/VV25mzsIpnl25h36E6CnN7cPVZRUwpLaZsUG80k69I6yhhSKd38HAd85ZtY86iTby4vJpDdfUM7pvFlNJippQVM7p/brJDFOkQlDCkS9lz8DDPLtnCnEVVvL5qO/UOZwzIZUpZMdeeVcygvlnJDlEkZSlhSJdVXVPL3MWbmb1wE2+v3wXAuUP6MKW0mKvOLKIgVyPlisRSwhABNuzcz5xFVTy5qIrlW2pIM7hwZD5TSou5csIA8jK7JTtEkaRTwhBp5P2tNcxZWMXsRZvYsPMA3TPS+PiYQqaUFfPxMwrJ7KZuutI1KWGInIS7s3DDrlDz2Mz2vbXk9MjgE+OjZzwuHJlPt3QNiCBdhxKGSBzq6p03V+9gzsIq5i7ZTM3BI/TN7s7VZxYxpayYcwf3IU3PeEgnl7SEYWaDgIeAAUA9cJ+7/9TM+gK/BYYCa4HPuPuHYZ87gBuBOuAWd382lJ/LsTm95wK3egvBK2HIqao9UsfLK6qZs6iKF5Zt5eDheop7ZXJtaTGfGN+fsUV5ZHXXQAnS+SQzYRQBRe7+tpnlAguAqcDfADvd/U4zux3o4+5fN7NxwCPAJKAYeAEY7e51ZjYfuBV4kyhh3O3uTzd3fiUMaQv7ao/w/HtbmbOoilfer+ZIvZNmMLwghwnFeYwv7sX4kjzGF/WiV5ZunEvHlrSxpNx9M7A5LNeY2TKgBKgALg2bzQReAr4eyh9191pgjZmtAiaZ2Vogz93fADCzh4gST7MJQ6QtZPfIYOrZJUw9u4QP9x3iL2t3srRqD0urdvPWmp08sbDq6LYD+/RkQnEvxhfnMb4kjwnFvSjMy0xi9CJtp93q1GY2FDgbeAvoH5IJ7r7ZzArDZiVENYgGG0PZ4bDcuLyp88wAZgAMHjy4Da9ABPpkd+cT4wfwifEDjpbt2FsbEsgellTt5r2qPTyzdMvR9fk5PZhQkhclkeJeTCjuxaC+PTVsiXQ47ZIwzCwH+D1wm7vvaeYfSlMrvJnyEwvd7wPug6hJqvXRirROv5weXDy6gItHFxwtqzl4mGWba1hatZslm6LayKsrt1NXH/0vmZuZcTSBjC/OY0JJL4bnZ5OhHlmSwhKeMMysG1GyeNjdHw/FW82sKNQuioBtoXwjMChm94FAVSgf2ES5SErKzezGpGF9mTSs79Gyg4freH9rzdEEsrRqD79+cx21R+oB6JGRxtiimJpISR6j++fqmRBJGQlNGBZVJR4Alrn7XTGr5gDTgTvD++yY8t+Y2V1EN71HAfPDTe8aMzuPqEnrBuBniYxdpK1ldkvnrIG9OWtg76NlR+rqWb19H0s27T56X2TOoioefms9ABlpxsjCnONqImOLcsnVU+mSBInuJfVR4FVgMVG3WoBvEP3ozwIGA+uBT7v7zrDPN4EvAkeImrCeDuXlHOtW+zTwZXWrlc7I3dmw8wBLqnYfrYks2bSH7Xtrj24ztF8WE0p6UTqwN2cO7MWEkl7kaAZCaQN6cE+kE9i252CURDZFN9eXbNrDpl0HADCDEQU5nDWwF2eV9OKsQb0ZV5Sn5ixpNU3RKtIJFOZl8vG8TD5+Rv+jZdU1tSzZtJtFG3exeONuXnl/O4+/vQmImrNG98+ldFAvzizpzVkDezFmQK6GOpFTphqGSCfi7mzZc5BFG3azeNMu3t24m3c37mb3gcMAdM9IY1xRXlQTGRglkREFOZrWVo5Sk5RIF+burN+5PySPKIks2bSbfYfqAMjqns6E4l6cNbAXZw6M7osM6Zel50S6KDVJiXRhZsaQftkM6ZfNtaXFANTXO6u37w01kahJ61cxXXzzMjM4K9xQLx3YizMH9qa4V6aSSBenGoaIAHC4rp6VW/dGtZBNUW1k+eYajoSHDfNzunNmybGmrLMG9taMhZ2Qahgi0qJu6WmMK85jXHEe00LZwcN1LN9Sw+KNu1i0cTeLN+7m5fdXEnIIRb0yGVmYw9B+2QzplxVqMlkM7pulHlqdkBKGiJxUZrd0ygb1pmxQb/46lO2rPcJ7m/ewaMMuFm/azZrt+5i9cBN7Dh45bt8BeZkhiRxLJEP7ZTO4X5amw+2glDBEpFWye2QwcWhfJg7te1z5rv2HWLdjP2t37GP9jv2s3bGf9Tv38eKKaqprNh63bd/s7gzum8XQflkM7pfN0JjE0i+7u+6VpCglDBFpE72zutM7qzulg3qfsG5f7RHW79zPuh37WbdjH+t2Ru+V6z5kzqKqo01cANnd04/WSI69R8tFeZma9TCJlDBEJOGye2QwtiiPsUV5J6w7dKSejR/uP1o7aUgqK7bW8MKyrRyuO5ZNumekMahPz6NNW7Hvg/r01Gi/CaaEISJJ1T0jjeEFOQwvyDlhXV29s3n3gZBEQu0kJJY3Vu9gf3iWBKBbujG0XzYjCnIYWZjDiMJoeURBDtkaZ6tN6FsUkZSVnmYM7JPFwD5ZXDjy+HXuTvXeWtbv2M+a7fv4oHofH1Tv5f1tNTy/bOvRuUfgWG+uEQU5jCjMYURBNiMLcijI7aH7Ja2ghCEiHZKZUZibSWFuJuWNbsAfOlLP+p37WLVtb5RItu1lVfVefle54egT7hBNZHW0RnL0PZvBfbPUvNUEJQwR6XS6Z6QxsjCXkYW5x5U3jLX1wbaoNhIllL288n41jy041pOrqeatkQW5DC/I7tLNW133ykWkyzEzinr1pKhXTz46Kv+4dXsOHuaDhhpJSCZxN28V5lCQ0/mbt5QwRESAvMxunD24D2cP7nNc+bHmrSiRNNe8Nbwgh4G9e1LcO5Pi3j0p7t2TkvDeJ6tbh08oShgiIs1ornlr657ao81aq7btZc32fSzbsod5y7dy8HD9cdtndks7lkB6RUmkqHfm0YRS1Csz5YdTUcIQETkFZsaAXpkM6JV5QvOWu/Ph/sNU7TrApl0HqDr6OsimXQd4ccs2ttXUnnDM/JzuUc0kJJTimIRS3Lsn/bK7J/XBxYQmDDP7BXANsM3dJ4SyvsBvgaHAWuAz7v5hWHcHcCNQB9zi7s+G8nM5Np/3XODWlubzFhFJFjOjb3Z3+mZ3Z0JJrya3qT1Sx9bdtccnlN0H2LTrIKuq9/LKyurjnjOBqLZT3Csz1Eh6UhLT9NWQYLK6J+5nPdE1jAeB/wIeiim7HZjn7nea2e3h89fNbBwwDRgPFAMvmNlod68D7gVmAG8SJYzJwNMJjl1EJGF6ZKQzuF8Wg/tlNbne3dl94HBIKAdjkkq0/OcPtrN1z8HjhlUB6JPVjcF9s3ji5gvb/J5JQhOGu79iZkMbFVcAl4blmcBLwNdD+aPuXgusMbNVwCQzWwvkufsbAGb2EDAVJQwR6cTM7Oj4XOOLm66lHK6rZ+ueg0cTSkNt5eDh+oTcYE/GPYz+7r4ZwN03m1lhKC8hqkE02BjKDoflxuVNMrMZRLURBg8e3IZhi4iklm7paUefhG8PqfQoY1Pp0Jspb5K73+fu5e5eXlBQ0GbBiYh0dclIGFvNrAggvG8L5RuBQTHbDQSqQvnAJspFRKQdJSNhzAGmh+XpwOyY8mlm1sPMhgGjgPmh+arGzM6zqFHuhph9RESknSS6W+0jRDe4881sI/CvwJ3ALDO7EVgPfBrA3Zea2SzgPeAIcHPoIQVwE8e61T6NbniLiLQ768yPM5SXl3tlZWWywxAR6TDMbIG7lze1LpVueouISApTwhARkbgoYYiISFw69T0MM6sG1iU7jtOUD2xPdhApQt/F8fR9HE/fxzGn810McfcmH2Lr1AmjMzCzypPdgOpq9F0cT9/H8fR9HJOo70JNUiIiEhclDBERiYsSRuq7L9kBpBB9F8fT93E8fR/HJOS70D0MERGJi2oYIiISFyUMERGJixJGCjKzQWb2opktM7OlZnZrsmNKBWaWbmbvmNlTyY4lmcyst5k9ZmbLw/8j5yc7pmQys6+EfydLzOwRM8tMdkztycx+YWbbzGxJTFlfM3vezFaG9z5tcS4ljNR0BPhndx8LnAfcHOY87+puBZYlO4gU8FPgGXc/AyilC38nZlYC3AKUu/sEIB2Yltyo2t2DwORGZbcD89x9FDAvfD5tShgpyN03u/vbYbmG6AfhpNPSdgVmNhC4Grg/2bEkk5nlARcDDwC4+yF335XUoJIvA+hpZhlAFl1sgjV3fwXY2ai4ApgZlmcCU9viXEoYKc7MhgJnA28lOZRk+wnwNaA+yXEk23CgGvhlaJ6738yykx1Usrj7JuDHRHPrbAZ2u/tzyY0qJfQPk88R3gvb4qBKGCnMzHKA3wO3ufueZMeTLGZ2DbDN3RckO5YUkAGcA9zr7mcD+2ij5oaOKLTNVwDDgGIg28yuT25UnZcSRooys25EyeJhd3882fEk2YXAFDNbCzwKfNzMfp3ckJJmI7DR3RtqnI8RJZCu6nJgjbtXu/th4HHggiTHlAq2mlkRQHjf1hYHVcJIQWHu8geAZe5+V7LjSTZ3v8PdB7r7UKIbmn9y9y75V6S7bwE2mNmYUHQZ0bTGXdV64Dwzywr/bi6jC3cCiDEHmB6WpwOz2+KgCZ3TW07ZhcBfA4vNbGEo+4a7z01eSJJCvgw8bGbdgdXAF5IcT9K4+1tm9hjwNlHvwnfoYkOEmNkjwKVAvpltBP4VuBOYZWY3EiXVT7fJuTQ0iIiIxENNUiIiEhclDBERiYsShoiIxEUJQ0RE4qKEISIicVHCkJRnZm5m/xnz+atm9p02OvaDZvZ/2uJYLZzn02Fk2RcTGZeZDTWzz7c+wmaP+ZiZDQ/Le5tYX2Bmz7TlOSU1KWFIR1ALfMrM8pMdSCwzS2/F5jcC/+juH0tUPMFQoFUJo7nrMLPxQLq7rz7ZNu5eDWw2swtbc17peJQwpCM4QvQw1lcar2j8l3jDX8BmdqmZvWxms8zsfTO708yuM7P5ZrbYzEbEHOZyM3s1bHdN2D/dzH5kZn8xs3fN7O9jjvuimf0GWNxEPJ8Lx19iZj8MZf8CfBT4bzP7URP7fC3ss8jM7mxi/dqGZGlm5Wb2Uli+xMwWhtc7ZpZL9MDWRaHsK/Feh5llm9kfQwxLzOyz4fTX0cRTwmaWb2ZvmNnVoeiJsK10YnrSWzqKnwPvmtn/bcU+pcBYoqGfVwP3u/skiyak+jJwW9huKHAJMAJ40cxGAjcQjXw60cx6AK+bWcMoqJOACe6+JvZkZlYM/BA4F/gQeM7Mprr7v5nZx4Gvuntlo30+STT09Efcfb+Z9W3F9X0VuNndXw8DVR4kGojwq+7ekPhmxHMdZvZXQJW7Xx326xW2uRB4pFHM/YmGnviWuz8fiiuB77UidumAVMOQDiGM1vsQ0WQ58fpLmFukFvgAaPihXEyUJBrMcvd6d19JlFjOAD4B3BCGZnkL6AeMCtvPb5wsgonAS2EgvCPAw0RzVzTncuCX7r4/XGfjeQ2a8zpwl5ndAvQO52ws3utYTFTT+qGZXeTuu0N5EdFw6g26EU3I87WYZAHR4HbFrYhdOiAlDOlIfkJ0LyB2/ocjhP+Pw+Bz3WPW1cYs18d8ruf42nXj8XEcMODL7l4WXsNi5lnYd5L4LM7raLxPS+PzHL1G4Oj0o+5+J/C3QE/gTTM74yTHb/E63P19oprRYuA/QjMawIHYc4ZYFgBXNjpPZthWOjElDOkwwl/fs4iSRoO1RD90EM2L0O0UDv1pM0sL9zWGAyuAZ4GbLBpmHjMbbS1PVPQWcElo308HPge83MI+zwFfNLOscJ6mmqTWcuwa/6qh0MxGuPtid/8hUZPQGUANkBuzb1zXEZrT9rv7r4kmJGoYMn0ZMDJmUwe+CJxhZrHzcIwGliCdmu5hSEfzn8CXYj7/LzDbzOYTNZWc7K//5qwg+mHvD/yDux80s/uJmq3eDjWXalqY5tLdN5vZHcCLRH/Zz3X3ZoeVdvdnzKwMqDSzQ8Bc4BuNNvsu8ICZfYPjZ168zcw+BtQRDXH+NFHt6YiZLSKa6/mncV7HmcCPzKweOAzcFMr/SDQS6gsxMdeZ2TTgSTPb4+73AB8L20onptFqReSkzKwnUQK80N3rmtnuFaDC3T9st+Ck3SlhiEizzOxKosm81p9kfQFRQnmiXQOTdqeEISIicdFNbxERiYsShoiIxEUJQ0RE4qKEISIicVHCEBGRuPw/hn9GwMhvPKEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#to find no of clusters\n",
+    "wcss_list= []  \n",
+    "    \n",
+    "for i in range(1, 11):  \n",
+    "    kmeans = KMeans(n_clusters=i, init='k-means++', random_state= 42)  \n",
+    "    kmeans.fit(x)  \n",
+    "    wcss_list.append(kmeans.inertia_)  \n",
+    "plt.plot(range(1, 11), wcss_list)  \n",
+    "plt.title('The Elbow Method Graph')  \n",
+    "plt.xlabel('Number of clusters(k)')  \n",
+    "plt.ylabel('wcss_list')  \n",
+    "plt.show()  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c47867b0",
+   "metadata": {},
+   "source": [
+    "## KMeans to visualize clusters in the PCA scatter plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "1baaa137",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(n_clusters=3, init='k-means++', random_state= 42)  \n",
+    "y_predict= kmeans.fit_predict(x)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "20e8570c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x20091208220>"
+      ]
+     },
+     "execution_count": 186,
+     "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": [
+    "#visualising the scatter plot\n",
+    "plt.scatter(pca_1, pca_2, c=kmeans.labels_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b5ee772",
+   "metadata": {},
+   "source": [
+    "## Visualize the feature differences between the clusters "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "id": "99902e4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='percent', ylabel='measurement'>"
+      ]
+     },
+     "execution_count": 187,
+     "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": [
+    "df1['cluster'] = kmeans.labels_\n",
+    "\n",
+    "# Reshape the DataFrame to the long format\n",
+    "melt_car = pd.melt(df1, id_vars='cluster', \n",
+    "                   var_name='measurement', \n",
+    "                   value_name='percent', \n",
+    "                   value_vars=x)\n",
+    "\n",
+    "# Create a violin plot splitting and coloring the results according to the km-clusters\n",
+    "sns.barplot(x = melt_car['percent'], y = melt_car['measurement'], hue = melt_car['cluster'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1c02429",
+   "metadata": {},
+   "source": [
+    "## Compute the number of accidents within each cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "id": "63e65fc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = pd.read_csv('miles-driven.csv', sep='|')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "51964cc2",
+   "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>drvr_fatl_col_bmiles</th>\n",
+       "      <th>perc_fatl_speed</th>\n",
+       "      <th>perc_fatl_alcohol</th>\n",
+       "      <th>perc_fatl_1st_time</th>\n",
+       "      <th>cluster</th>\n",
+       "      <th>million_miles_annually</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>18.8</td>\n",
+       "      <td>39</td>\n",
+       "      <td>30</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>64914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>18.1</td>\n",
+       "      <td>41</td>\n",
+       "      <td>25</td>\n",
+       "      <td>94</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>35</td>\n",
+       "      <td>28</td>\n",
+       "      <td>96</td>\n",
+       "      <td>2</td>\n",
+       "      <td>59575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>22.4</td>\n",
+       "      <td>18</td>\n",
+       "      <td>26</td>\n",
+       "      <td>95</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>28</td>\n",
+       "      <td>89</td>\n",
+       "      <td>2</td>\n",
+       "      <td>320784</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state  drvr_fatl_col_bmiles  perc_fatl_speed  perc_fatl_alcohol  \\\n",
+       "0     Alabama                  18.8               39                 30   \n",
+       "1      Alaska                  18.1               41                 25   \n",
+       "2     Arizona                  18.6               35                 28   \n",
+       "3    Arkansas                  22.4               18                 26   \n",
+       "4  California                  12.0               35                 28   \n",
+       "\n",
+       "   perc_fatl_1st_time  cluster  million_miles_annually  \n",
+       "0                  80        0                   64914  \n",
+       "1                  94        2                    4593  \n",
+       "2                  96        2                   59575  \n",
+       "3                  95        1                   32953  \n",
+       "4                  89        2                  320784  "
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_df=df1.merge(df2)\n",
+    "final_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "id": "e0bafbef",
+   "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>drvr_fatl_col_bmiles</th>\n",
+       "      <th>perc_fatl_speed</th>\n",
+       "      <th>perc_fatl_alcohol</th>\n",
+       "      <th>perc_fatl_1st_time</th>\n",
+       "      <th>cluster</th>\n",
+       "      <th>million_miles_annually</th>\n",
+       "      <th>num_drvr_fatl_col</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>18.8</td>\n",
+       "      <td>39</td>\n",
+       "      <td>30</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>64914</td>\n",
+       "      <td>1220.3832</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>18.1</td>\n",
+       "      <td>41</td>\n",
+       "      <td>25</td>\n",
+       "      <td>94</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4593</td>\n",
+       "      <td>83.1333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>35</td>\n",
+       "      <td>28</td>\n",
+       "      <td>96</td>\n",
+       "      <td>2</td>\n",
+       "      <td>59575</td>\n",
+       "      <td>1108.0950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>22.4</td>\n",
+       "      <td>18</td>\n",
+       "      <td>26</td>\n",
+       "      <td>95</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32953</td>\n",
+       "      <td>738.1472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>28</td>\n",
+       "      <td>89</td>\n",
+       "      <td>2</td>\n",
+       "      <td>320784</td>\n",
+       "      <td>3849.4080</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state  drvr_fatl_col_bmiles  perc_fatl_speed  perc_fatl_alcohol  \\\n",
+       "0     Alabama                  18.8               39                 30   \n",
+       "1      Alaska                  18.1               41                 25   \n",
+       "2     Arizona                  18.6               35                 28   \n",
+       "3    Arkansas                  22.4               18                 26   \n",
+       "4  California                  12.0               35                 28   \n",
+       "\n",
+       "   perc_fatl_1st_time  cluster  million_miles_annually  num_drvr_fatl_col  \n",
+       "0                  80        0                   64914          1220.3832  \n",
+       "1                  94        2                    4593            83.1333  \n",
+       "2                  96        2                   59575          1108.0950  \n",
+       "3                  95        1                   32953           738.1472  \n",
+       "4                  89        2                  320784          3849.4080  "
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_df['num_drvr_fatl_col'] = (final_df['drvr_fatl_col_bmiles'] * final_df[\"million_miles_annually\"])/1000\n",
+    "final_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "3d92990d",
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cluster</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17</td>\n",
+       "      <td>951.709165</td>\n",
+       "      <td>16179.0558</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18</td>\n",
+       "      <td>990.046094</td>\n",
+       "      <td>17820.8297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16</td>\n",
+       "      <td>727.207806</td>\n",
+       "      <td>11635.3249</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         count        mean         sum\n",
+       "cluster                               \n",
+       "0           17  951.709165  16179.0558\n",
+       "1           18  990.046094  17820.8297\n",
+       "2           16  727.207806  11635.3249"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_df.groupby(\"cluster\")[\"num_drvr_fatl_col\"].agg([\"count\", \"mean\", \"sum\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "id": "cc86df79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='cluster', ylabel='num_drvr_fatl_col'>"
+      ]
+     },
+     "execution_count": 192,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEGCAYAAABPdROvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYFklEQVR4nO3df5BlZZ3f8fdHUCTKoMJozc5ABmWkAqhjmCVk2bVcyeroGsEEdEgWWGUzQiCrcdcEYiq6Vqis8QcJm5XKKBQ/CkEUFaoWdPFHNDEgNsjyU3QQkJYJDKCA64LO+M0f97l66enp6Tunu693+v2qOnXP/Z7znPs91QzfOs/znHNSVUiStLOeMeoEJEnjzUIiSerEQiJJ6sRCIknqxEIiSepk91EnMAr77rtvrVy5ctRpSNJYufHGGx+uqqVT44uykKxcuZKJiYlRpyFJYyXJfdPF7dqSJHViIZEkdWIhkSR1YiGRJHViIZEkdWIhkSR1YiGRJHViIZEkdWIhkSR1Mq93tic5H3gj8FBVHdpinwIOars8D/hxVa1OshK4E7irbbu+qk5pbQ4DLgD2BK4G3llVlWQP4CLgMOAR4K1Vde98npPGyw8+8LJRp7DL2/8/3TrqFDRi831FcgGwdjBQVW+tqtVVtRq4AvjswOa7+9v6RaQ5F1gPrGpL/5gnAz+qqgOBs4EPzstZSJK2a14LSVV9HXh0um1JArwFuHSmYyRZBiypquuq917gi4Bj2uajgQvb+meAo9pxJUkLZJRjJL8DPFhV3xuIHZDk20m+luR3Wmw5MDmwz2SL9bfdD1BVW4DHgH2m+7Ek65NMJJnYvHnzXJ6HJC1qoywkx/P0q5FNwP5V9Urg3cAnkywBprvCqPY507anB6s2VNWaqlqzdOk2T0GWJO2kkTxGPsnuwD+jN0gOQFU9BTzV1m9McjfwUnpXICsGmq8AHmjrk8B+wGQ75t5spytNkjQ/RnVF8k+A71TVL7uskixNsltbfzG9QfXvV9Um4IkkR7TxjxOBK1uzq4CT2vqxwFfaOIokaYHMayFJcilwHXBQkskkJ7dN69h2kP1VwC1J/obewPkpVdW/ujgV+ASwEbgbuKbFzwP2SbKRXnfYGfN2MpKkac1r11ZVHb+d+B9OE7uC3nTg6fafAA6dJv4kcFy3LCVJXXhnuySpEwuJJKkTC4kkqRMLiSSpEwuJJKkTC4kkqRMLiSSpEwuJJKkTC4kkqRMLiSSpk5E8/XecHPaei0adwi7vxg+dOOoUJHXgFYkkqRMLiSSpEwuJJKkTC4kkqRMLiSSpEwuJJKkTC4kkqRMLiSSpEwuJJKmTeS0kSc5P8lCS2wZi70/ywyQ3t+UNA9vOTLIxyV1JXjcQPyzJrW3bOUnS4nsk+VSLfzPJyvk8H0nStub7iuQCYO008bOranVbrgZIcjCwDjiktflYkt3a/ucC64FVbekf82TgR1V1IHA28MH5OhFJ0vTmtZBU1deBR2e5+9HAZVX1VFXdA2wEDk+yDFhSVddVVQEXAccMtLmwrX8GOKp/tSJJWhijGiM5Pcktrevr+S22HLh/YJ/JFlve1qfGn9amqrYAjwH7TPeDSdYnmUgysXnz5rk7E0la5EZRSM4FXgKsBjYBH2nx6a4kaob4TG22DVZtqKo1VbVm6dKlQyUsSdq+BS8kVfVgVW2tql8AHwcOb5smgf0Gdl0BPNDiK6aJP61Nkt2BvZl9V5okaQ4seCFpYx59bwb6M7quAta1mVgH0BtUv6GqNgFPJDmijX+cCFw50Oaktn4s8JU2jiJJWiDz+mKrJJcCrwb2TTIJvA94dZLV9Lqg7gXeAVBVtye5HLgD2AKcVlVb26FOpTcDbE/gmrYAnAdcnGQjvSuRdfN5PpKkbc1rIamq46cJnzfD/mcBZ00TnwAOnSb+JHBclxwlSd14Z7skqRMLiSSpEwuJJKkTC4kkqRMLiSSpEwuJJKkTC4kkqRMLiSSpEwuJJKkTC4kkqRMLiSSpEwuJJKkTC4kkqRMLiSSpEwuJJKkTC4kkqRMLiSSpEwuJJKkTC4kkqZMdvrM9ya1ATbcJqKp6+ZxnJUkaGzssJMAbd/bgSc5v7R+qqkNb7EPAPwV+BtwNvK2qfpxkJXAncFdrfn1VndLaHAZcAOwJXA28s6oqyR7ARcBhwCPAW6vq3p3NV5I0vB12bVXVff0FeBJ4WVv+rsVmcgGwdkrsWuDQdiXzXeDMgW13V9XqtpwyED8XWA+sakv/mCcDP6qqA4GzgQ/u6HwkSXNr1mMkSd4C3AAcB7wF+GaSY2dqU1VfBx6dEvvrqtrSvl4PrNjB7y4DllTVdVVV9K5AjmmbjwYubOufAY5KktmekySpu9l0bfW9F/jNqnoIIMlS4Ev0/ge+s94OfGrg+wFJvg08DvzHqvrfwHJgcmCfyRajfd4PUFVbkjwG7AM83CEnSdIQhikkz+gXkeYROsz6SvJeYAtwSQttAvavqkfamMjnkxxCb1B/qv7g/0zbpv7eenrdY+y///47m7YkaYphCskXknwRuLR9fytwzc78aJKT6A3CH9W6q6iqp4Cn2vqNSe4GXkrvCmSw+2sF8EBbnwT2AyaT7A7szZSutL6q2gBsAFizZs20xUaSNLxZX1FU1XuA/wm8HHgFsKGq/t2wP5hkLfDvgTdV1U8H4kuT7NbWX0xvUP37VbUJeCLJEW3840TgytbsKuCktn4s8JV+YZIkLYxZX5EkOQC4uqo+277vmWTlTNNtk1wKvBrYN8kk8D56s7T2AK5t4+L9ab6vAj6QZAuwFTilqvpXF6fyq+m/1/CrK6HzgIuTbKR3JbJutucjSZobw3RtfRr4rYHvW1vsN7fXoKqOnyZ83nb2vQK4YjvbJoBDp4k/SW8WmSRpRIYZLN+9qn7W/9LWnzX3KUmSxskwhWRzkjf1vyQ5GqfZStKiN0zX1inAJUn+R/s+CZww9ylJksbJrAtJVd0NHJHkuUCq6onB7UlOqqoLp28tSdpVDX1DYVX9ZGoRad45B/lIksbMMF1bO+IzriTNmSP/4shRp7AofOPffKPzMebyxVbeCChJi9BcFhKvSCRpEZrLQtL9+kiSNHZm86rdd8+0vao+2j5Pn6ukJEnjYzaD7XvNsM1xEUla5HZYSKrqzwCSHFlVT+u+SuK0Ckla5IYZI/mLWcYkSYvIbMZI/jG9p/4unTJesgTYbb4SkySNh9mMkTwLeG7bd3C85HF6L5OSJC1isxkj+RrwtSQXVNV9C5CTJGmMDPOIlJ8m+RBwCPDsfrCqXjPnWUmSxsYwg+2XAN8BDgD+DLgX+NY85CRJGiPDFJJ9quo84OdV9bWqejtwxDzlJUkaE8N0bf28fW5K8vvAA8CKuU9JkjROhrki+c9J9gb+BPhT4BPAv52pQZLzkzyU5LaB2AuSXJvke+3z+QPbzkyyMcldSV43ED8sya1t2zlJ0uJ7JPlUi38zycohzkeSNAd2WEiSfLCt7llVj1XVbVX1u1V1WFVdtYPmFwBrp8TOAL5cVauAL7fvJDkYWEdvMH8t8LEk/ftUzgXWA6va0j/mycCPqupA4Gygn6skaYHM5orkDUmeCZw57MGr6uvAo1PCRwP9V/JeCBwzEL+sqp6qqnuAjcDhSZYBS6rquqoq4KIpbfrH+gxwVP9qRZK0MGYzRvIF4GHgOUkeH4gHqKpaMuRvvqiqNtFrvCnJC1t8OXD9wH6TLfbztj413m9zfzvWliSPAfu0fCVJC2CHVyRV9Z6q2hv4q6paMrDstRNFZCbTXUnUDPGZ2mx78GR9kokkE5s3b97JFCVJU816sL2qjp5pe5LrZnmoB1t3Fe3zoRafBPYb2G8FvZlhkzx9dlg//rQ2SXYH9mbbrrR+/huqak1VrVm6dOksU5Uk7chcviHx2TveBYCrgJPa+knAlQPxdW0m1gH0BtVvaN1gTyQ5oo1/nDilTf9YxwJfaeMokqQFMsx9JDuyzf/Ak1wKvBrYN8kk8D7gz4HLk5wM/AA4DqCqbk9yOXAHsAU4raq2tkOdSm8G2J7ANW0BOA+4OMlGelci6+bwfCRJszCXhWQbVXX8djYdtZ39zwLOmiY+ARw6TfxJWiGSJI3GXHZtOe1WkhahWRWSJLsl+dIOdjthDvKRJI2ZWRWSNlbx0/aIlO3tc9v2tkmSdl3DjJE8Cdya5Frgb/vBqvrjOc9KkjQ2hikkf9UWSZJ+aZhC8jhwdVU9NV/JSJLGzzCztt4EfDfJxUl+v91JLkla5IZ5RMrbgAOBTwP/Arg7ySfmKzFJ0ngY6qqiqn6e5Bp6d7HvSe8x7n80H4lJksbDrK9IkqxNcgG994QcS+8NicvmKS9J0pgY5orkJOBTwDsccJck9c2qkLRX3i6tqs/PbzqSpHEzZ3e2S5IWJ+9slyR14p3tkqROZl1IqurC+UxEkjSedlhIktzKNG8/7Kuql89pRpKksTKbK5I3ts/T2ufF7fNfAj+d84wkSWNlh4Wkqu4DSHJkVR05sOmMJN8APjBfyUmSfv0N89DG5yT57f6XJL8FPGfuU5IkjZNhCsnJwF8muTfJPcDHgLfvzI8mOSjJzQPL40neleT9SX44EH/DQJszk2xMcleS1w3ED0tya9t2ThLfHS9JC2iYWVs3Aq9IsgRIVT02uD3JSbOd2VVVdwGrW7vdgB8CnwPeBpxdVR+ecuyDgXXAIcBvAF9K8tJ2o+S5wHrgeuBqYC1wzWzPS5LUzTBXJABU1eNTi0jzzp3M4Sjg7v5YzHYcDVxWVU9V1T30Hhx5eJJlwJKquq6qCrgIOGYn85Ak7YShC8kMdrZLaR1w6cD305PckuT8JM9vseXA/QP7TLbY8rY+NS5JWiBzWUi2e6/J9iR5Fr03L366hc4FXkKv22sT8JH+rtv5ve3Fp/ut9Ukmkkxs3rx52FQlSdsx6iuS1wM3VdWDAFX1YFVtrapfAB8HDm/7TQL7DbRbATzQ4iumiW+jqjZU1ZqqWrN06dKdSFWSNJ25LCTf2Ik2xzPQrdXGPPreDNzW1q8C1iXZI8kBwCrghqraBDyR5Ig2W+tE4Mqdyl6StFNmPWsryfPo/Y965WC7/tN/q+r0YX44yd8Dfg94x0D4vyZZTa976t7+tqq6PcnlwB3AFuC0NmML4FTgAnqv/r0GZ2xJ0oIa5um/V9ObYnsr8IuuP1xVPwX2mRI7YYb9zwLOmiY+ARzaNR9J0s4ZppA8u6rePW+ZSJLG0jBjJBcn+VdJliV5QX+Zt8wkSWNhmCuSnwEfAt7Lr6bYFvDiuU5KkjQ+hikk7wYOrKqH5ysZSdL4GaZr63Z8/4gkaYphrki2Ajcn+SrwVD/Yn/4rSVqchikkn2+LJEm/NMxj5Gf1iHhJ0uIyzJ3t9zDNAxGryllbkrSIDdO1tWZg/dnAcYD3kUjSIjfrWVtV9cjA8sOq+m/Aa+YvNUnSOBima+sfDnx9Br0rlL3mPCNJ0lgZpmvrI/xqjGQLvafzHjfXCUmSxsswheT1wD/n6Y+RXwd8YI5zkiSNkWHvI/kxcBPw5HwkI0kaP8MUkhVVtXbeMpEkjaVhnrX1f5O8bN4ykSSNpWGuSH4b+MN2Y+JTQICqqpfPS2aSpLEw7GC7JElPM8yztu6bz0QkSeNpmDESSZK2MbJCkuTeJLcmuTnJRIu9IMm1Sb7XPp8/sP+ZSTYmuSvJ6wbih7XjbExyTpKM4nwkabEa9RXJ71bV6qrqPxDyDODLVbUK+HL7TpKD6d38eAiwFvhYkt1am3OB9cCqtjhFWZIW0KgLyVRHA/33nlwIHDMQv6yqnqqqe4CNwOFJlgFLquq6qirgooE2kqQFMMpCUsBfJ7kxyfoWe1FVbQJony9s8eXA/QNtJ1tseVufGt9GkvVJJpJMbN68eQ5PQ5IWt2Gm/861I6vqgSQvBK5N8p0Z9p1u3KNmiG8brNoAbABYs2bNtPtIkoY3siuSqnqgfT4EfA44HHiwdVfRPh9qu08C+w00XwE80OIrpolLkhbISApJkuck2au/DrwWuA24Cjip7XYScGVbvwpYl2SPJAfQG1S/oXV/PZHkiDZb68SBNpKkBTCqrq0XAZ9rM3V3Bz5ZVV9I8i3g8iQnAz+gve+kqm5PcjlwB713oZxWVVvbsU4FLgD2BK5piyRpgYykkFTV94FXTBN/BDhqO23OAs6aJj4BHDrXOUqSZufXbfqvJGnMWEgkSZ1YSCRJnVhIJEmdWEgkSZ1YSCRJnVhIJEmdWEgkSZ1YSCRJnVhIJEmdWEgkSZ1YSCRJnVhIJEmdWEgkSZ1YSCRJnVhIJEmdWEgkSZ1YSCRJnVhIJEmdWEgkSZ2MpJAk2S/JV5PcmeT2JO9s8fcn+WGSm9vyhoE2ZybZmOSuJK8biB+W5Na27ZwkGcU5SdJitfuIfncL8CdVdVOSvYAbk1zbtp1dVR8e3DnJwcA64BDgN4AvJXlpVW0FzgXWA9cDVwNrgWsW6DwkadEbyRVJVW2qqpva+hPAncDyGZocDVxWVU9V1T3ARuDwJMuAJVV1XVUVcBFwzPxmL0kaNPIxkiQrgVcC32yh05PckuT8JM9vseXA/QPNJltseVufGp/ud9YnmUgysXnz5rk8BUla1EZaSJI8F7gCeFdVPU6vm+olwGpgE/CR/q7TNK8Z4tsGqzZU1ZqqWrN06dKuqUuSmpEVkiTPpFdELqmqzwJU1YNVtbWqfgF8HDi87T4J7DfQfAXwQIuvmCYuSVogo5q1FeA84M6q+uhAfNnAbm8GbmvrVwHrkuyR5ABgFXBDVW0CnkhyRDvmicCVC3ISkiRgdLO2jgROAG5NcnOL/Qfg+CSr6XVP3Qu8A6Cqbk9yOXAHvRlfp7UZWwCnAhcAe9KbreWMLUlaQCMpJFX1f5h+fOPqGdqcBZw1TXwCOHTuspMkDWPks7YkSePNQiJJ6sRCIknqxEIiSerEQiJJ6sRCIknqxEIiSerEQiJJ6sRCIknqxEIiSerEQiJJ6sRCIknqxEIiSerEQiJJ6sRCIknqxEIiSerEQiJJ6sRCIknqxEIiSerEQiJJ6mSXKCRJ1ia5K8nGJGeMOh9JWkzGvpAk2Q34S+D1wMHA8UkOHm1WkrR4jH0hAQ4HNlbV96vqZ8BlwNEjzkmSFo1U1ahz6CTJscDaqvqj9v0E4B9V1elT9lsPrG9fDwLuWtBEF9a+wMOjTkI7xb/deNvV/35/v6qWTg3uPopM5limiW1THatqA7Bh/tMZvSQTVbVm1HloeP7txtti/fvtCl1bk8B+A99XAA+MKBdJWnR2hULyLWBVkgOSPAtYB1w14pwkadEY+66tqtqS5HTgi8BuwPlVdfuI0xq1RdGFt4vybzfeFuXfb+wH2yVJo7UrdG1JkkbIQiJJ6sRCsgvxUTHjK8n5SR5Kctuoc9HwkuyX5KtJ7kxye5J3jjqnheQYyS6iPSrmu8Dv0ZsS/S3g+Kq6Y6SJaVaSvAr4CXBRVR066nw0nCTLgGVVdVOSvYAbgWMWy78/r0h2HT4qZoxV1deBR0edh3ZOVW2qqpva+hPAncDy0Wa1cCwku47lwP0D3ydZRP8hS78ukqwEXgl8c8SpLBgLya5jVo+KkTR/kjwXuAJ4V1U9Pup8FoqFZNfho2KkEUryTHpF5JKq+uyo81lIFpJdh4+KkUYkSYDzgDur6qOjzmehWUh2EVW1Beg/KuZO4HIfFTM+klwKXAcclGQyycmjzklDORI4AXhNkpvb8oZRJ7VQnP4rSerEKxJJUicWEklSJxYSSVInFhJJUicWEklSJxYSaZ4leX+SP92Jds9L8q/nIydpLllIpF9fzwOGKiTp8d+1FpT/wUlzLMmJSW5J8jdJLp6y7X8lWdPW901yb1s/JMkN7Ua2W5KsAv4ceEmLfajt954k32r7/FmLrWzvwfgYcBNPf1SONO92H3UC0q4kySHAe4Ejq+rhJC8A/ngWTU8B/ntVXdIecbMbcAZwaFWtbsd+LbCK3isDAlzV3mPyA+Ag4G1VZVeYFpyFRJpbrwE+U1UPA1TVo73HMO3QdcB7k6wAPltV35um3Wvb8u32/bn0CssPgPuq6vo5yF8aml1b0twKMz++fwu/+nf37H6wqj4JvAn4O+CLSV6znWP/l6pa3ZYDq+q8tu1vu6cu7RwLiTS3vgy8Jck+AK1ra9C9wGFt/dh+MMmLge9X1Tn0ntr8cuAJYK+Btl8E3t7eeUGS5UleOB8nIQ3Dri1pDlXV7UnOAr6WZCu9bqh7B3b5MHB5khOArwzE3wr8QZKfA/8P+EDrFvtGktuAa6rqPUn+AXBd6/b6CfAHwNZ5PzFpBj79V5LUiV1bkqROLCSSpE4sJJKkTiwkkqROLCSSpE4sJJKkTiwkkqRO/j9GHERPzVsqxwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x='cluster', y='num_drvr_fatl_col', data=final_df, estimator=sum, ci=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21798b23",
+   "metadata": {},
+   "source": [
+    "##  Make a decision when there is no clear right choice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7713f25",
+   "metadata": {},
+   "source": [
+    "\n",
+    "I chose cluster 1  as it has the highest count from overall clusters its due to number of fatal accidents from consuming alcohol\n",
+    "we need to put strict actions into consumption of alcohol while driving"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "id": "e89c0a04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3          Arkansas\n",
+       "9           Florida\n",
+       "10          Georgia\n",
+       "14          Indiana\n",
+       "15             Iowa\n",
+       "16           Kansas\n",
+       "17         Kentucky\n",
+       "21    Massachusetts\n",
+       "22         Michigan\n",
+       "23        Minnesota\n",
+       "24      Mississippi\n",
+       "27         Nebraska\n",
+       "30       New Jersey\n",
+       "31       New Mexico\n",
+       "34     North Dakota\n",
+       "35             Ohio\n",
+       "42        Tennessee\n",
+       "46         Virginia\n",
+       "Name: state, dtype: object"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_df.loc[final_df['cluster']==1]['state']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f26c1790",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 2769f20df126007b1dd03159d4a24d9343ae9b67 Mon Sep 17 00:00:00 2001
From: hirlekham28 <125371985+hirlekham28@users.noreply.github.com>
Date: Mon, 20 Feb 2023 19:22:20 +0530
Subject: [PATCH 3/3] Add files via upload

---
 miles-driven.csv   | 52 +++++++++++++++++++++++++++++++++++++++
 road-accidents.csv | 61 ++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 113 insertions(+)
 create mode 100644 miles-driven.csv
 create mode 100644 road-accidents.csv

diff --git a/miles-driven.csv b/miles-driven.csv
new file mode 100644
index 0000000..0252130
--- /dev/null
+++ b/miles-driven.csv
@@ -0,0 +1,52 @@
+state|million_miles_annually
+Alabama|64914
+Alaska|4593
+Arizona|59575
+Arkansas|32953
+California|320784
+Colorado|46606
+Connecticut|31197
+Delaware|9028
+District of Columbia|3568
+Florida|191855
+Georgia|108454
+Hawaii|10066
+Idaho|15937
+Illinois|103234
+Indiana|76485
+Iowa|31274
+Kansas|30021
+Kentucky|48061
+Louisiana|46513
+Maine|14248
+Maryland|56221
+Massachusetts|54792
+Michigan|94754
+Minnesota|56685
+Mississippi|38851
+Missouri|68789
+Montana|11660
+Nebraska|19093
+Nevada|24189
+New Hampshire|12720
+New Jersey|73094
+New Mexico|25650
+New York|127726
+North Carolina|103772
+North Dakota|9131
+Ohio|111990
+Oklahoma|47464
+Oregon|33373
+Pennsylvania|99204
+Rhode Island|7901
+South Carolina|48730
+South Dakota|9002
+Tennessee|70751
+Texas|237440
+Utah|26222
+Vermont|7141
+Virginia|80974
+Washington|56955
+West Virginia|18963
+Wisconsin|58554
+Wyoming|9245
diff --git a/road-accidents.csv b/road-accidents.csv
new file mode 100644
index 0000000..4afb30b
--- /dev/null
+++ b/road-accidents.csv
@@ -0,0 +1,61 @@
+##### LICENSE #####
+# This data set is modified from the original at fivethirtyeight (https://github.com/fivethirtyeight/data/tree/master/bad-drivers)
+# and it is released under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
+##### COLUMN ABBREVIATIONS #####
+# drvr_fatl_col_bmiles = Number of drivers involved in fatal collisions per billion miles (2011)
+# perc_fatl_speed = Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding (2009)
+# perc_fatl_alcohol = Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired (2011)
+# perc_fatl_1st_time = Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents (2011)
+##### DATA BEGIN #####
+state|drvr_fatl_col_bmiles|perc_fatl_speed|perc_fatl_alcohol|perc_fatl_1st_time
+Alabama|18.8|39|30|80
+Alaska|18.1|41|25|94
+Arizona|18.6|35|28|96
+Arkansas|22.4|18|26|95
+California|12|35|28|89
+Colorado|13.6|37|28|95
+Connecticut|10.8|46|36|82
+Delaware|16.2|38|30|99
+District of Columbia|5.9|34|27|100
+Florida|17.9|21|29|94
+Georgia|15.6|19|25|93
+Hawaii|17.5|54|41|87
+Idaho|15.3|36|29|98
+Illinois|12.8|36|34|96
+Indiana|14.5|25|29|95
+Iowa|15.7|17|25|87
+Kansas|17.8|27|24|85
+Kentucky|21.4|19|23|76
+Louisiana|20.5|35|33|98
+Maine|15.1|38|30|84
+Maryland|12.5|34|32|99
+Massachusetts|8.2|23|35|80
+Michigan|14.1|24|28|77
+Minnesota|9.6|23|29|88
+Mississippi|17.6|15|31|100
+Missouri|16.1|43|34|84
+Montana|21.4|39|44|85
+Nebraska|14.9|13|35|90
+Nevada|14.7|37|32|99
+New Hampshire|11.6|35|30|83
+New Jersey|11.2|16|28|78
+New Mexico|18.4|19|27|98
+New York|12.3|32|29|80
+North Carolina|16.8|39|31|81
+North Dakota|23.9|23|42|86
+Ohio|14.1|28|34|82
+Oklahoma|19.9|32|29|94
+Oregon|12.8|33|26|90
+Pennsylvania|18.2|50|31|88
+Rhode Island|11.1|34|38|79
+South Carolina|23.9|38|41|81
+South Dakota|19.4|31|33|86
+Tennessee|19.5|21|29|81
+Texas|19.4|40|38|87
+Utah|11.3|43|16|96
+Vermont|13.6|30|30|95
+Virginia|12.7|19|27|88
+Washington|10.6|42|33|86
+West Virginia|23.8|34|28|87
+Wisconsin|13.8|36|33|84
+Wyoming|17.4|42|32|90