generated from mwc/project_argument
626 lines
20 KiB
Plaintext
626 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "worldwide-blood",
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"metadata": {},
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"source": [
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"# Introduction"
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]
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},
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{
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"cell_type": "markdown",
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"id": "understanding-numbers",
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"metadata": {},
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"source": [
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"*✏️ Write 2-3 sentences describing your research.*\n",
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"\n",
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"It's a collection of data on the reasons fatal car crashes occur in every state of America, and it will be used to determine which region of America is the deadliest. "
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]
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},
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{
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"cell_type": "markdown",
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"id": "greater-circular",
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"metadata": {},
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"source": [
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"## Overarching Question: What is the deadliest region in America to drive on?"
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]
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},
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{
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"cell_type": "markdown",
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"id": "appreciated-testimony",
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"metadata": {},
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"source": [
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"*✏️ Write 2-3 sentences explaining why this question.*\n",
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"\n",
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"I am interested in this because I live on the Northeast Coast and we have a lot of car \n",
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"accidents. People drive very fast here. The roads are not always paved properly and maintained. I want to know if it's just bad luck when people get into accidents or if it's their own fault. "
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]
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},
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{
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"cell_type": "markdown",
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"id": "permanent-pollution",
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"metadata": {},
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"source": [
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"# Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "technical-evans",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Include any import statements you will need\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "overhead-sigma",
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"metadata": {},
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"outputs": [],
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"source": [
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"### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
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"\n",
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"file_name = \"bad-drivers.csv\"\n",
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"dataset_path = \"data/\" + file_name\n",
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"\n",
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"df = pd.read_csv(dataset_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "heated-blade",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>State</th>\n",
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" <th>Number of drivers involved in fatal collisions per billion miles</th>\n",
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" <th>Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding</th>\n",
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" <th>Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired</th>\n",
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" <th>Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted</th>\n",
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" <th>Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents</th>\n",
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" <th>Car Insurance Premiums ($)</th>\n",
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" <th>Losses incurred by insurance companies for collisions per insured driver ($)</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Alabama</td>\n",
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" <td>18.8</td>\n",
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" <td>39</td>\n",
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" <td>30</td>\n",
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" <td>96</td>\n",
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" <td>80</td>\n",
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" <td>784.55</td>\n",
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" <td>145.08</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Alaska</td>\n",
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" <td>18.1</td>\n",
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" <td>41</td>\n",
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" <td>25</td>\n",
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" <td>90</td>\n",
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" <td>94</td>\n",
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" <td>1053.48</td>\n",
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" <td>133.93</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Arizona</td>\n",
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" <td>18.6</td>\n",
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" <td>35</td>\n",
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" <td>28</td>\n",
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" <td>84</td>\n",
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" <td>96</td>\n",
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" <td>899.47</td>\n",
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" <td>110.35</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Arkansas</td>\n",
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" <td>22.4</td>\n",
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" <td>18</td>\n",
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" <td>26</td>\n",
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" <td>94</td>\n",
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" <td>95</td>\n",
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" <td>827.34</td>\n",
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" <td>142.39</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>California</td>\n",
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" <td>12.0</td>\n",
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" <td>35</td>\n",
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" <td>28</td>\n",
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" <td>91</td>\n",
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" <td>89</td>\n",
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" <td>878.41</td>\n",
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" <td>165.63</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" State \\\n",
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"0 Alabama \n",
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"1 Alaska \n",
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"2 Arizona \n",
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"3 Arkansas \n",
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"4 California \n",
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"\n",
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" Number of drivers involved in fatal collisions per billion miles \\\n",
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"0 18.8 \n",
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"1 18.1 \n",
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"2 18.6 \n",
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"3 22.4 \n",
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"4 12.0 \n",
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"\n",
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" Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n",
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"0 39 \n",
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"1 41 \n",
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"2 35 \n",
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"3 18 \n",
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"4 35 \n",
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"\n",
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" Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n",
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"0 30 \n",
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"1 25 \n",
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"2 28 \n",
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"3 26 \n",
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"4 28 \n",
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"\n",
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" Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n",
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"0 96 \n",
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"1 90 \n",
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"2 84 \n",
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"3 94 \n",
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"4 91 \n",
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"\n",
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" Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n",
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"0 80 \n",
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"1 94 \n",
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"2 96 \n",
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"3 95 \n",
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"4 89 \n",
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"\n",
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" Car Insurance Premiums ($) \\\n",
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"0 784.55 \n",
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"1 1053.48 \n",
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"2 899.47 \n",
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"3 827.34 \n",
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"4 878.41 \n",
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"\n",
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" Losses incurred by insurance companies for collisions per insured driver ($) \n",
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"0 145.08 \n",
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"1 133.93 \n",
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"2 110.35 \n",
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"3 142.39 \n",
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"4 165.63 "
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "continental-franklin",
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"metadata": {},
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"source": [
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"**Data Overview**\n",
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"\n",
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"*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*\n",
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"\n",
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"I got the data set from FiveThirtyEight. It was used for an article called\n",
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"\"Dear Mona, Which state has the worst drivers?\" in October 2014. The person who wrote the article is Mona Chalabi, they are a data editor at the Guardian US, \n",
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"a columnist at New York Margazine, and a lead news writer for FiveThirtyEight.\n",
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"\n",
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"The date is about fatal collisions in each state. There are 7 rows, some of the rows\n",
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"are about \"Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\" and \"Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\"\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6ba44c9c-60d1-46a4-8257-b4e8eeea348d",
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"metadata": {},
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"source": [
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"I will recategorise the data so that all of the states data will be separated into the five regions of the United States"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"id": "f7bba5f3-5911-4a76-ad43-f6ce78cd4fb3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>State</th>\n",
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" <th>Number of drivers involved in fatal collisions per billion miles</th>\n",
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" <th>Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding</th>\n",
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" <th>Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired</th>\n",
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" <th>Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted</th>\n",
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" <th>Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents</th>\n",
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" <th>Car Insurance Premiums ($)</th>\n",
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" <th>Losses incurred by insurance companies for collisions per insured driver ($)</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>32</th>\n",
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" <td>New York</td>\n",
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" <td>12.3</td>\n",
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" <td>32</td>\n",
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" <td>29</td>\n",
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" <td>88</td>\n",
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" <td>80</td>\n",
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" <td>1234.31</td>\n",
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" <td>150.01</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" State \\\n",
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"32 New York \n",
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"\n",
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" Number of drivers involved in fatal collisions per billion miles \\\n",
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"32 12.3 \n",
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"\n",
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" Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n",
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"32 32 \n",
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"\n",
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" Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n",
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"32 29 \n",
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"\n",
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" Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n",
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"32 88 \n",
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"\n",
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" Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n",
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"32 80 \n",
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"\n",
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" Car Insurance Premiums ($) \\\n",
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"32 1234.31 \n",
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"\n",
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" Losses incurred by insurance companies for collisions per insured driver ($) \n",
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"32 150.01 "
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]
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},
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"execution_count": 40,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"Northeast = df[df.State == \"New York\"]\n",
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"Northeast"
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]
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},
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{
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"cell_type": "markdown",
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"id": "infinite-instrument",
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"metadata": {},
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"source": [
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"# Methods and Results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "basic-canadian",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Import any helper files you need here"
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]
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},
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{
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"cell_type": "markdown",
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"id": "recognized-positive",
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"metadata": {},
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"source": [
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"## First Research Question: Is drinking and driving the biggest cause of fatal collisions?\\"
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]
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},
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{
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"cell_type": "markdown",
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"id": "graduate-palmer",
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"metadata": {},
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"source": [
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"### Methods"
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]
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},
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{
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"cell_type": "markdown",
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"id": "endless-variation",
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"metadata": {},
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"source": [
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"*Explain how you will approach this research question below. Consider the following:* \n",
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" - *Which aspects of the dataset will you use?* \n",
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" - *How will you reorganize/store the data?* \n",
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" - *What data science tools/functions will you use and why?* \n",
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" \n",
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"✏️ *Write your answer below:*\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "portuguese-japan",
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"metadata": {},
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"source": [
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"### Results "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "negative-highlight",
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"metadata": {},
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"outputs": [],
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"source": [
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"#######################################################################\n",
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"### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
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"### \n",
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"### Your data analysis may include a statistic and/or a data visualization\n",
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"#######################################################################"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "victorian-burning",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
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]
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},
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{
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"cell_type": "markdown",
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"id": "collectible-puppy",
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"metadata": {},
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"source": [
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"## Second Research Question: What state is the most unluckiest state for fatel collisions?\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "demographic-future",
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"metadata": {},
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"source": [
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"### Methods"
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]
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},
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{
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"cell_type": "markdown",
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"id": "incorporate-roller",
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"metadata": {},
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"source": [
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"*Explain how you will approach this research question below. Consider the following:* \n",
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" - *Which aspects of the dataset will you use?* \n",
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" - *How will you reorganize/store the data?* \n",
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" - *What data science tools/functions will you use and why?* \n",
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"\n",
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"✏️ *Write your answer below:*\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "juvenile-creation",
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"metadata": {},
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"source": [
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"### Results "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "pursuant-surrey",
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"metadata": {},
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"outputs": [],
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"source": [
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"#######################################################################\n",
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"### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
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"###\n",
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"### Your data analysis may include a statistic and/or a data visualization\n",
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"#######################################################################"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "located-night",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
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]
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},
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{
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"cell_type": "markdown",
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"id": "infectious-symbol",
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"metadata": {},
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"source": [
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"# Discussion"
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]
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},
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{
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"cell_type": "markdown",
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"id": "furnished-camping",
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"metadata": {
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"code_folding": []
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},
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"source": [
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"## Considerations"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bearing-stadium",
|
|
"metadata": {},
|
|
"source": [
|
|
"*It's important to recognize the limitations of our research.\n",
|
|
"Consider the following:*\n",
|
|
"\n",
|
|
"- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
|
|
"- *What were limitations of your datset?*\n",
|
|
"- *Are there any known biases in the data?*\n",
|
|
"\n",
|
|
"✏️ *Write your answer below:*"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "beneficial-invasion",
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|
"metadata": {},
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|
"source": [
|
|
"## Summary"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "about-raise",
|
|
"metadata": {},
|
|
"source": [
|
|
"*Summarize what you discovered through the research. Consider the following:*\n",
|
|
"\n",
|
|
"- *What did you learn about your media consumption/digital habits?*\n",
|
|
"- *Did the results make sense?*\n",
|
|
"- *What was most surprising?*\n",
|
|
"- *How will this project impact you going forward?*\n",
|
|
"\n",
|
|
"✏️ *Write your answer below:*"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
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|
"jupytext": {
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|
"cell_metadata_json": true,
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"text_representation": {
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"extension": ".Rmd",
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"format_name": "rmarkdown",
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"format_version": "1.2",
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"jupytext_version": "1.9.1"
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}
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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|
"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.7"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": false,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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|
},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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|
"library": "var_list.r",
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|
"varRefreshCmd": "cat(var_dic_list()) "
|
|
}
|
|
},
|
|
"types_to_exclude": [
|
|
"module",
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|
"function",
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"builtin_function_or_method",
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|
"instance",
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"_Feature"
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|
],
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|
"window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|