FinalProject_group092.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - The Effect of COVID-19 in Our Oceans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the growing prominence of climate change and the beginning of the pandemic, we wanted to analyze the influence of COVID-19 preventative measures on the amount of pollution found in the United States' oceans, more specifically Personal Protective Equipment (PPE). After looking through past work regarding ocean waste, including National Oceanic and Atmospheric Administration (NOAA), we found a dataset from TIDES, or Trash Information and Data for Educaiton and Solutions. Using this data, we cleaned it of its few missing variables and constructed some features to determine the potential correlation between amounts of and rates of PPE collected over time; with a t-test, we found that the rates of PPE collected before and after the start of COVID-19 were not signfiicantly different. After analyzing the relationship between the variables, we used some of these features to construct a predictor model, calculating the potential amounts of PPE based off of past trends. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Sikandar Bangash\n",
    "- Ian Jeffers\n",
    "- Mary Mei Longano\n",
    "- Rachel Ung"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How has COVID-19 preventative measures affected the amounts of and rates of PPE pollution collected on the coastlines of the United States over time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plastic pollution has been on the rise for years, and has impacted the ecosystems in our oceans drastically. While these pollutants may be only one of many factors impacting our environment, plastic pollution is one of the most impactful issues. From sea turtles choking on plastic straws to microplastics showing up in major human organs, plastic pollution seems to permeate our lives in ways we never would have imagined. Nowadays, in the era of the coronavirus pandemic, masks and Personal Protective Equipment (PPE) are normalized and encouraged, which is leaving its mark in the form of plastic PPE pollution. Using historical data from the last few years, we hope to analyze this trend to further understand how our ecosystems have and will be affected by human plastic consumption and waste.\n",
    "\n",
    "The National Centers for Environmental Information, National Oceanic and Atmospheric Administration (NOAA) is a source for environmental data and has specifically recorded trends occuring in the oceans due to plastic consumption. Their site features data regarding sea surface salinity and temperature, microplastics, pCO2, and chlorophyll collected from Turn the Tide on Plastic and AkzoNobel and meteorological data. Additionally, they feature data including ocean currents and estimates the plastics in locations using currents and identifies it by plastic subtype and pollutant type. Another source of information was the Coastal Cleanup Project, which is a volunteer-driven waste-cleanup and waste-tracking organization, who has been operating and collecting pollution and litter data since 2015. \n",
    "\n",
    "On March 11, 2020, the World Health Organization officially declared COVID-19 to be a pandemic. We want to investigate if the implementation of COVID-19 preventative measures has any correlation with the amount of Personal Protective Equipment (PPE) found in our oceans.\n",
    "\n",
    "References (include links):\n",
    "- 1) https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.nodc:0170967\n",
    "- 2) https://hub.arcgis.com/datasets/9975c79018ac43ea88ddb64ff92e5375?geometry=-143.690%2C-88.949%2C-32.176%2C63.454\n",
    "- 3) https://www.ajmc.com/view/a-timeline-of-covid19-developments-in-2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We believe that there will be an increase in PPE pollution (measured as frequency and PPE collected per volunteer) in the ocean as time progresses from the start of the COVID-19 pandemic.\n",
    "\n",
    "(prompted by the drastic increase in both the supply and demand of PPE in accordance with local policy and regulation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset comes form TIDES, or Trash Information and Data for Educaiton and Solutions. We are focused on the United States of America specifically, and we intend to use the waste data, including weight and volume of trash cleaned, to understand the distribution of trash in our oceans.\n",
    "\n",
    "- Dataset Name: [Data] Trash Information and Data for Education and Solutions\n",
    "- Link to the dataset: https://www.coastalcleanupdata.org/reports#\n",
    "- Number of observations: 20,086"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "from scipy.stats import ttest_ind\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cleanup ID</th>\n",
       "      <th>Zone</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>GPS</th>\n",
       "      <th>Cleanup Type</th>\n",
       "      <th>Cleanup Date</th>\n",
       "      <th>Group Name</th>\n",
       "      <th>Adults</th>\n",
       "      <th>Children</th>\n",
       "      <th>...</th>\n",
       "      <th>Condoms</th>\n",
       "      <th>Diapers</th>\n",
       "      <th>Syringes</th>\n",
       "      <th>Tampons/Tampon Applicators</th>\n",
       "      <th>Personal Hygiene (Clean Swell)</th>\n",
       "      <th>Gloves &amp; Masks (PPE)</th>\n",
       "      <th>Foam Pieces</th>\n",
       "      <th>Glass Pieces</th>\n",
       "      <th>Plastic Pieces</th>\n",
       "      <th>Total Items Collected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75961</td>\n",
       "      <td>Monroe County, MI, USA</td>\n",
       "      <td>Michigan, USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>41.93515, -83.39225</td>\n",
       "      <td>Land (beach, shoreline and inland)</td>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>28.635674604683, -83.133995150659</td>\n",
       "      <td>Land (beach, shoreline and inland)</td>\n",
       "      <td>2020-09-19</td>\n",
       "      <td>Citrus County Aquatic Services</td>\n",
       "      <td>130.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>283</td>\n",
       "      <td>179</td>\n",
       "      <td>192</td>\n",
       "      <td>6445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102671</td>\n",
       "      <td>Harris County, TX, USA</td>\n",
       "      <td>Texas, USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>29.70636, -95.54475</td>\n",
       "      <td>Land (beach, shoreline and inland)</td>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>Kinsey-Lloyd</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102672</td>\n",
       "      <td>Harris County, TX, USA</td>\n",
       "      <td>Texas, USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>29.70636, -95.54475</td>\n",
       "      <td>Land (beach, shoreline and inland)</td>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>Kinsey-Lloyd</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1251</td>\n",
       "      <td>1302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102682</td>\n",
       "      <td>Harris County, TX, USA</td>\n",
       "      <td>Texas, USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>29.70636, -95.54475</td>\n",
       "      <td>Land (beach, shoreline and inland)</td>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>Kinsey-Lloyd</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>262.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cleanup ID                    Zone          State        Country  \\\n",
       "0      75961  Monroe County, MI, USA  Michigan, USA  United States   \n",
       "1      94571                     NaN            NaN  United States   \n",
       "2     102671  Harris County, TX, USA     Texas, USA  United States   \n",
       "3     102672  Harris County, TX, USA     Texas, USA  United States   \n",
       "4     102682  Harris County, TX, USA     Texas, USA  United States   \n",
       "\n",
       "                                 GPS                        Cleanup Type  \\\n",
       "0                41.93515, -83.39225  Land (beach, shoreline and inland)   \n",
       "1  28.635674604683, -83.133995150659  Land (beach, shoreline and inland)   \n",
       "2                29.70636, -95.54475  Land (beach, shoreline and inland)   \n",
       "3                29.70636, -95.54475  Land (beach, shoreline and inland)   \n",
       "4                29.70636, -95.54475  Land (beach, shoreline and inland)   \n",
       "\n",
       "  Cleanup Date                      Group Name  Adults  Children  ...  \\\n",
       "0   2020-06-22                             NaN     1.0       0.0  ...   \n",
       "1   2020-09-19  Citrus County Aquatic Services   130.0     121.0  ...   \n",
       "2   2020-12-24                    Kinsey-Lloyd     2.0       0.0  ...   \n",
       "3   2020-12-24                    Kinsey-Lloyd     2.0       0.0  ...   \n",
       "4   2020-12-24                    Kinsey-Lloyd     2.0       0.0  ...   \n",
       "\n",
       "   Condoms  Diapers  Syringes  Tampons/Tampon Applicators  \\\n",
       "0        0        0         0                           0   \n",
       "1        7        3         1                          12   \n",
       "2        0        0         0                           0   \n",
       "3        0        0         0                           0   \n",
       "4        0        0         0                           0   \n",
       "\n",
       "   Personal Hygiene (Clean Swell)  Gloves & Masks (PPE)  Foam Pieces  \\\n",
       "0                               0                     0            0   \n",
       "1                               0                     0          283   \n",
       "2                               0                     0            0   \n",
       "3                               2                     0            0   \n",
       "4                               0                     0            0   \n",
       "\n",
       "   Glass Pieces  Plastic Pieces  Total Items Collected  \n",
       "0             0               0                   11.0  \n",
       "1           179             192                 6445.0  \n",
       "2             0             250                  262.0  \n",
       "3             0            1251                 1302.0  \n",
       "4             0             250                  262.0  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('DetailedSummary-UnitedStates.csv', low_memory=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will look at the features in this dataset, we can see there is the Cleanup ID, Zone, State, Country, GPS, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Cleanup ID', 'Zone', 'State', 'Country', 'GPS', 'Cleanup Type',\n",
       "       'Cleanup Date', 'Group Name', 'Adults', 'Children', 'People', 'Pounds',\n",
       "       'Miles', '# of bags', 'Cigarette Butts',\n",
       "       'Food Wrappers (candy, chips, etc.)',\n",
       "       'Take Out/Away Containers (Plastic)', 'Take Out/Away Containers (Foam)',\n",
       "       'Bottle Caps (Plastic)', 'Bottle Caps (Metal)', 'Lids (Plastic)',\n",
       "       'Straws, Stirrers', 'Forks, Knives, Spoons',\n",
       "       'Beverage Bottles (Plastic)', 'Beverage Bottles (Glass)',\n",
       "       'Beverage Cans', 'Grocery Bags (Plastic)', 'Other Plastic Bags',\n",
       "       'Paper Bags', 'Cups, Plates (Paper)', 'Cups, Plates (Plastic)',\n",
       "       'Cups, Plates (Foam)', 'Fishing Buoys, Pots & Traps',\n",
       "       'Fishing Net & Pieces', 'Fishing Line (1 yard/meter = 1 piece)',\n",
       "       'Rope (1 yard/meter = 1 piece)', 'Fishing Gear (Clean Swell)',\n",
       "       '6-Pack Holders', 'Other Plastic/Foam Packaging',\n",
       "       'Other Plastic Bottles (oil, bleach, etc.)', 'Strapping Bands',\n",
       "       'Tobacco Packaging/Wrap', 'Other Packaging (Clean Swell)',\n",
       "       'Beverages Sachets', 'Appliances (refrigerators, washers, etc.)',\n",
       "       'Balloons', 'Cigar Tips', 'Cigarette Lighters',\n",
       "       'Construction Materials', 'Fireworks', 'Tires', 'Toys',\n",
       "       'Other Trash (Clean Swell)', 'E-cigarettes',\n",
       "       'Other tobacco (packaging, lighter, etc.)', 'Condoms', 'Diapers',\n",
       "       'Syringes', 'Tampons/Tampon Applicators',\n",
       "       'Personal Hygiene (Clean Swell)', 'Gloves & Masks (PPE)', 'Foam Pieces',\n",
       "       'Glass Pieces', 'Plastic Pieces', 'Total Items Collected'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon further investigation, we can see that many of the variables are in the int or float representations with a few as objects or strings. Manually looking at the dataset, it makes sense that a majority of the variables are integers, as many of the values are frequency of a type of pollution e.g. '# of bags' or 'Cigarette Butts'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int64      50\n",
       "object      8\n",
       "float64     7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Missingness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will investigate the null values, looking at the number of null values in each observation, or cleanup, and those in the dataset's features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5195, 65)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_rows = df[df.isnull().any(axis=1)]\n",
    "null_rows.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Zone', 'State', 'Country', 'GPS', 'Cleanup Type', 'Cleanup Date',\n",
       "       'Group Name', 'Adults', 'Children', 'People', 'Pounds', 'Miles',\n",
       "       '# of bags', 'Total Items Collected'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_cols = df.columns[df.isnull().any()]\n",
    "null_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Zone                        9\n",
       "State                       9\n",
       "Country                     1\n",
       "GPS                         1\n",
       "Cleanup Type                1\n",
       "Cleanup Date                1\n",
       "Group Name               5191\n",
       "Adults                      1\n",
       "Children                    1\n",
       "People                      1\n",
       "Pounds                      1\n",
       "Miles                       1\n",
       "# of bags                   1\n",
       "Total Items Collected       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[null_cols].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, there are 14 of the features that have at least one missing value, 'Group Name' having the most missing values. With 'Group Name', we do not believe this will be incredibly relevant to our posed question. Because there are very few missing values in the other columns, we will simply drop the missing values for simplicity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20086, 64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping unnecessary columns: \n",
    "df = df.drop(labels=['Group Name'], axis=1)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Adults</th>\n",
       "      <th>Children</th>\n",
       "      <th>People</th>\n",
       "      <th>Pounds</th>\n",
       "      <th>Miles</th>\n",
       "      <th># of bags</th>\n",
       "      <th>Cigarette Butts</th>\n",
       "      <th>Food Wrappers (candy, chips, etc.)</th>\n",
       "      <th>Take Out/Away Containers (Plastic)</th>\n",
       "      <th>Take Out/Away Containers (Foam)</th>\n",
       "      <th>...</th>\n",
       "      <th>Condoms</th>\n",
       "      <th>Diapers</th>\n",
       "      <th>Syringes</th>\n",
       "      <th>Tampons/Tampon Applicators</th>\n",
       "      <th>Personal Hygiene (Clean Swell)</th>\n",
       "      <th>Gloves &amp; Masks (PPE)</th>\n",
       "      <th>Foam Pieces</th>\n",
       "      <th>Glass Pieces</th>\n",
       "      <th>Plastic Pieces</th>\n",
       "      <th>Total Items Collected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.00000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.964188</td>\n",
       "      <td>0.32480</td>\n",
       "      <td>4.288987</td>\n",
       "      <td>92.701432</td>\n",
       "      <td>1.131988</td>\n",
       "      <td>2.962245</td>\n",
       "      <td>24.857349</td>\n",
       "      <td>12.143846</td>\n",
       "      <td>2.606415</td>\n",
       "      <td>1.433132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030831</td>\n",
       "      <td>0.034816</td>\n",
       "      <td>0.023260</td>\n",
       "      <td>0.053743</td>\n",
       "      <td>1.589580</td>\n",
       "      <td>1.386711</td>\n",
       "      <td>2.377198</td>\n",
       "      <td>1.065697</td>\n",
       "      <td>24.542511</td>\n",
       "      <td>136.390995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.691491</td>\n",
       "      <td>5.90085</td>\n",
       "      <td>26.834194</td>\n",
       "      <td>2395.710077</td>\n",
       "      <td>28.682063</td>\n",
       "      <td>85.160284</td>\n",
       "      <td>112.109586</td>\n",
       "      <td>40.088129</td>\n",
       "      <td>9.629162</td>\n",
       "      <td>8.264842</td>\n",
       "      <td>...</td>\n",
       "      <td>0.706681</td>\n",
       "      <td>1.238951</td>\n",
       "      <td>0.630157</td>\n",
       "      <td>1.095486</td>\n",
       "      <td>6.507602</td>\n",
       "      <td>4.352584</td>\n",
       "      <td>27.900640</td>\n",
       "      <td>14.642186</td>\n",
       "      <td>107.281323</td>\n",
       "      <td>382.904062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.780000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.170000</td>\n",
       "      <td>0.051200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.629800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>146.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1773.000000</td>\n",
       "      <td>539.00000</td>\n",
       "      <td>1773.000000</td>\n",
       "      <td>216000.000000</td>\n",
       "      <td>2790.017300</td>\n",
       "      <td>9760.000000</td>\n",
       "      <td>5385.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>1355.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>5238.000000</td>\n",
       "      <td>22765.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Adults     Children        People         Pounds         Miles  \\\n",
       "count  20077.000000  20077.00000  20077.000000   20077.000000  20077.000000   \n",
       "mean       3.964188      0.32480      4.288987      92.701432      1.131988   \n",
       "std       24.691491      5.90085     26.834194    2395.710077     28.682063   \n",
       "min        0.000000      0.00000      0.000000       0.000000      0.000000   \n",
       "25%        1.000000      0.00000      1.000000       1.780000      0.000000   \n",
       "50%        1.000000      0.00000      1.000000       5.170000      0.051200   \n",
       "75%        2.000000      0.00000      2.000000      15.000000      0.629800   \n",
       "max     1773.000000    539.00000   1773.000000  216000.000000   2790.017300   \n",
       "\n",
       "          # of bags  Cigarette Butts  Food Wrappers (candy, chips, etc.)  \\\n",
       "count  20077.000000     20077.000000                        20077.000000   \n",
       "mean       2.962245        24.857349                           12.143846   \n",
       "std       85.160284       112.109586                           40.088129   \n",
       "min        0.000000         0.000000                            0.000000   \n",
       "25%        0.000000         0.000000                            0.000000   \n",
       "50%        0.000000         1.000000                            2.000000   \n",
       "75%        0.000000        14.000000                           11.000000   \n",
       "max     9760.000000      5385.000000                         2000.000000   \n",
       "\n",
       "       Take Out/Away Containers (Plastic)  Take Out/Away Containers (Foam)  \\\n",
       "count                        20077.000000                     20077.000000   \n",
       "mean                             2.606415                         1.433132   \n",
       "std                              9.629162                         8.264842   \n",
       "min                              0.000000                         0.000000   \n",
       "25%                              0.000000                         0.000000   \n",
       "50%                              0.000000                         0.000000   \n",
       "75%                              2.000000                         1.000000   \n",
       "max                            400.000000                       500.000000   \n",
       "\n",
       "       ...       Condoms       Diapers      Syringes  \\\n",
       "count  ...  20077.000000  20077.000000  20077.000000   \n",
       "mean   ...      0.030831      0.034816      0.023260   \n",
       "std    ...      0.706681      1.238951      0.630157   \n",
       "min    ...      0.000000      0.000000      0.000000   \n",
       "25%    ...      0.000000      0.000000      0.000000   \n",
       "50%    ...      0.000000      0.000000      0.000000   \n",
       "75%    ...      0.000000      0.000000      0.000000   \n",
       "max    ...     54.000000    150.000000     42.000000   \n",
       "\n",
       "       Tampons/Tampon Applicators  Personal Hygiene (Clean Swell)  \\\n",
       "count                20077.000000                    20077.000000   \n",
       "mean                     0.053743                        1.589580   \n",
       "std                      1.095486                        6.507602   \n",
       "min                      0.000000                        0.000000   \n",
       "25%                      0.000000                        0.000000   \n",
       "50%                      0.000000                        0.000000   \n",
       "75%                      0.000000                        1.000000   \n",
       "max                     96.000000                      400.000000   \n",
       "\n",
       "       Gloves & Masks (PPE)   Foam Pieces  Glass Pieces  Plastic Pieces  \\\n",
       "count          20077.000000  20077.000000  20077.000000    20077.000000   \n",
       "mean               1.386711      2.377198      1.065697       24.542511   \n",
       "std                4.352584     27.900640     14.642186      107.281323   \n",
       "min                0.000000      0.000000      0.000000        0.000000   \n",
       "25%                0.000000      0.000000      0.000000        0.000000   \n",
       "50%                0.000000      0.000000      0.000000        1.000000   \n",
       "75%                1.000000      0.000000      0.000000       16.000000   \n",
       "max              164.000000   1355.000000    981.000000     5238.000000   \n",
       "\n",
       "       Total Items Collected  \n",
       "count           20077.000000  \n",
       "mean              136.390995  \n",
       "std               382.904062  \n",
       "min                 0.000000  \n",
       "25%                 1.000000  \n",
       "50%                39.000000  \n",
       "75%               146.000000  \n",
       "max             22765.000000  \n",
       "\n",
       "[8 rows x 57 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# very little missing data, so we will drop the columns with missing values\n",
    "missingdata = df.isna().sum()\n",
    "missingdata.head()\n",
    "\n",
    "df = df.dropna(axis=0).reset_index(drop=True)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have constructed a feature that determines if the cleanup occurred before or on/after the start of COVID-19 (March 11, 2020)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['After_Covid'] = df['Cleanup Date'].apply(lambda x: x >= '2020-03-11')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will convert the 'Cleanup Date' feature to a datetime object. This representation will be useful for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cleanup Date'] = df['Cleanup Date'].apply(lambda x: datetime.strptime(str(x), '%Y-%m-%d'))\n",
    "df['Cleanup Date'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 'Cleanup Type'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we are looking at the type of Cleanup with the feature 'Cleanup Type'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Land (beach, shoreline and inland)', 'Underwater',\n",
       "       'Watercraft (powerboat, sailboat, kayak or canoe)'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cleanup Type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this feature, we can see that a majority of the cleanups occurred on land rather than on water. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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McEIwM7PECcHMzAAnBDMzS1aaECT9TtIbkibXlF0s6Z+SJqbHp2vOfUfSVElTJB1eU95f0nPp3JWSlMrXl3RLKn9CUt0afo9mZtYMzakhXA8c0Uj5FRHRNz3uBZC0GzAE6J3uuVpS+3T9NcAZwE7pUf+apwNvR8SOwBXAj1fxvZiZ2WpYaUKIiEeBt5r5ekcDIyPiw4h4FZgK7C2pJ9A1Ih6PiAD+ABxTc88N6fltwKH1tQczM2s9q9OH8HVJk1KT0iapbCvg9ZprZqSyrdLzZcsb3BMRi4B5QPfViMvMzFbBqiaEa4AdgL7ALOBnqbyxb/bRRHlT9yxH0hmSxksaP2fOnBYFbGZmTVulhBARsyNicUQsAX4N7J1OzQC2rrm0FzAzlfdqpLzBPZI6AN1YQRNVRFwXEQMiYkCPHj1WJXQzM1uBVUoIqU+g3rFA/Qiku4AhaeTQdhSdx09GxCxgvqRBqX9gKPCnmnuGpefHAw+lfgYzM2tFHVZ2gaSbgYOBzSTNAC4CDpbUl6JpZzrwHwAR8bykW4EXgEXAWRGxOL3UmRQjljoD96UHwG+BGyVNpagZDFkD78vMzFpopQkhIk5qpPi3TVw/AhjRSPl4YPdGyhcAJ6wsDjMzW7s8U9nMzAAnBDMzS5wQzMwMcEIwM7PECcHMzAAnBDMzS5wQzMwMcEIwM7PECcHMzAAnBDMzS5wQzMwMcEIwM7PECcHMzAAnBDMzS5wQzMwMcEIwM7PECcHMzAAnBDMzS5wQzMwMcEIwM7PECcHMzAAnBDMzS5wQzMwMcEIwM7PECcHMzAAnBDMzS5wQzMwMcEIwM7PECcHMzIBmJARJv5P0hqTJNWWbSnpA0svp5yY1574jaaqkKZIOrynvL+m5dO5KSUrl60u6JZU/IaluDb9HMzNrhubUEK4HjlimbDjwYETsBDyYjpG0GzAE6J3uuVpS+3TPNcAZwE7pUf+apwNvR8SOwBXAj1f1zZiZ2apbaUKIiEeBt5YpPhq4IT2/ATimpnxkRHwYEa8CU4G9JfUEukbE4xERwB+Wuaf+tW4DDq2vPZiZWetZ1T6ELSJiFkD6uXkq3wp4vea6Galsq/R82fIG90TEImAe0L2xXyrpDEnjJY2fM2fOKoZuZmaNWdOdyo19s48mypu6Z/nCiOsiYkBEDOjRo8cqhmhmZo1Z1YQwOzUDkX6+kcpnAFvXXNcLmJnKezVS3uAeSR2AbizfRGVmZmvZqiaEu4Bh6fkw4E815UPSyKHtKDqPn0zNSvMlDUr9A0OXuaf+tY4HHkr9DGZm1oo6rOwCSTcDBwObSZoBXAT8CLhV0unAa8AJABHxvKRbgReARcBZEbE4vdSZFCOWOgP3pQfAb4EbJU2lqBkMWSPvzMzMWmSlCSEiTlrBqUNXcP0IYEQj5eOB3RspX0BKKGZmlo9nKpuZGeCEYGZmiROCmZkBTghmZpY4IZiZGeCEYGZmiROCmZkBTghmZpY4IZiZGeCEYGZmiROCmZkBTghmZpY4IZiZGeCEYGZmiROCmZkBTghmZpY4IZiZGeCEYGZmiROCmZkBTghmZpY4IZiZGeCEYGZmiROCmZkBTghmZpY4IZiZGeCEYGZmiROCmZkBTghmZpasVkKQNF3Sc5ImShqfyjaV9ICkl9PPTWqu/46kqZKmSDq8prx/ep2pkq6UpNWJy8zMWm5N1BAOiYi+ETEgHQ8HHoyInYAH0zGSdgOGAL2BI4CrJbVP91wDnAHslB5HrIG4zMysBdZGk9HRwA3p+Q3AMTXlIyPiw4h4FZgK7C2pJ9A1Ih6PiAD+UHOPmZm1ktVNCAH8RdIESWeksi0iYhZA+rl5Kt8KeL3m3hmpbKv0fNny5Ug6Q9J4SePnzJmzmqGbmVmtDqt5//4RMVPS5sADkl5q4trG+gWiifLlCyOuA64DGDBgQKPXmJnZqlmtGkJEzEw/3wDuAPYGZqdmINLPN9LlM4Cta27vBcxM5b0aKTczs1a0yglBUhdJG9U/Bw4DJgN3AcPSZcOAP6XndwFDJK0vaTuKzuMnU7PSfEmD0uiioTX3mJlZK1mdJqMtgDvSCNEOwP8XEX+W9BRwq6TTgdeAEwAi4nlJtwIvAIuAsyJicXqtM4Hrgc7AfelhZmataJUTQkRMA/ZspHwucOgK7hkBjGikfDyw+6rGYmZmq88zlc3MDHBCMDOzxAnBzMwAJwQzM0ucEMzMDHBCMDOzxAnBzMwAJwQzM0ucEMzMDHBCMDOzxAnBzMwAJwQzM0ucEMzMDHBCMDOzxAnBzMwAJwQzM0ucEMzMDHBCMDOzxAnBzMwAJwQzM0ucEMzMDHBCMDOzxAnBzMwAJwQzM0ucEMzMDHBCMDOzxAnBzMwAJwQzM0ucEMzMDChRQpB0hKQpkqZKGp47HjOzqilFQpDUHrgKOBLYDThJ0m55ozIzq5YOuQNI9gamRsQ0AEkjgaOBF7JGZWbZ1Q2/J3cIa9X0Hx2VO4SlypIQtgJerzmeAeyz7EWSzgDOSIf/kjSlFWLLZTPgzdb6Zfpxa/2mSmjVv52tcev6v71tV3SiLAlBjZTFcgUR1wHXrf1w8pM0PiIG5I7DWs5/u7atyn+/UvQhUNQItq457gXMzBSLmVkllSUhPAXsJGk7SR2BIcBdmWMyM6uUUjQZRcQiSV8H7gfaA7+LiOczh5VbJZrG1lH+27Vtlf37KWK5pnozM6ugsjQZmZlZZk4IZmYGOCGYmVnihFAykrqkpTysjZDUXtIfc8dhq07SJpJ6S9peUmU/F0sxyqjK0v98Q4CTgYHAh8D6kuYA9wLXRcTLGUO0lYiIxZJ6SOoYEf/OHY81j6RuwFnASUBHYA7QCdhC0t+AqyPi4YwhtjonhPweBkYD3wEmR8QSAEmbAocAP5J0R0T4G2i5TQfGSboLeK++MCIuzxaRrcxtwB+AAyPindoTkvoDX5K0fUT8NkdwOXjYaWaS1ouIhat7jeUl6aLGyiPi+60di9mqckLILNUEVigi3mqtWGz1SeoSEe+t/EorC0miaLLdPiIukbQN8LGIeDJzaK2usp0nJTIBGJ9+zgH+Drycnk/IGJe1gKR9Jb0AvJiO95R0deawrHmuBval6EsAmE+xP0vlOCFkFhHbRcT2FMt2fDYiNouI7sBngNvzRmct8HPgcGAuQEQ8CxyUMyBrtn0i4ixgAUBEvE3RyVw5TgjlMTAi7q0/iIj7gE9kjMdaKCJeX6ZocZZArKUWpqHeASCpB7Akb0h5OCGUx5uSviupTtK2kv6T9G3T2oTXJe0HhKSOkr5Naj6y0rsSuINiuOkIYCxwad6Q8nCnckmkzuWL+KiZ4VHg++5UbhskbQb8N/BJig2f/gKc7b9f2yBpF+DQdPhQRFQymXseQkmkD45zcsdhq2zniDi5tkDS/sC4TPFYy6zPRzs3VrL/AFxDKA1JHwe+DdRRk6gjYnCumKz5JD0dEXutrMzKR9I5wFeBURRJ4ViKFQJ+kTWwDJwQSkLSs8C1FENNl3ZGRoSHnpaYpH2B/YBvAlfUnOoKHBsRe+aIy5pP0iRg3/r5I5K6AI9HRJ+8kbU+NxmVx6KIuCZ3ENZiHYENKf4tbVRT/i5wfJaIrKVEwxFhi/mo+ahSXEMoCUkXA29QjHb4sL7cnZJtg6RtI+IfueOwlpN0HjCM4t8ewDHA9RHx81wx5eKEUBKSXm2kONKkNSu5NHb9AqA3xYqZgPuA2oq0mN3+FDWDRyPimcwhZeEmo5KIiO1yx2Cr5SbgFooZ5l+j+MY5J2tE1hITgVmkz0RJ20TEa1kjysA1hBKRtDuwGw2/Yf4hX0TWXJImRER/SZPqOyMlPRIRnm1ecpK+QTEHaDYf9R+EO5Utm7R88sEUCeFe4EiKGZNOCG1D/fLksyQdBcwEemWMx5rvHIp5JJVfGcAJoTyOB/YEnomIUyVtAfwmc0zWfD9MO3B9C/gFxbDTc/OGZM30OjAvdxBl4IRQHh9ExBJJiyR1pRhx5A7ltmN0RCyg+GA5JHcw1iLTgDGS7qHhCL/K7XbnhFAe4yVtDPyaYnLav4DKbdDRhk2WNBt4jGIdqnER4W+dbcNr6dGRCi9bAe5ULiVJdUDXiJiUOxZrvrTT1oEUwxc/DbwTEX2zBmXWAq4hZCZphWvdSNorIp5uzXhs1UjqRZEIDqToC3qeYlCAlZznkHzECSG/nzVxLoDK/U/ZRr0GPAVcGhFfyx2MtYjnkCRuMjJbAyTtCRxAsZ/FNhT7Yj8SEb/NGpitlOeQfMQ1hBJJO27V0XD5a89DaAMi4llJrwCvUDQbnUKRHJwQys9zSBInhJKQdCOwA8UU+vqVFwNPTGsTJI2n2GTlrxR9Bwd5sbs2w3NIEjcZlYSkF4Hdwn+QNkdSO+DkiLgxdyxmq6Nd7gBsqcnAx3IHYS0XEUsodtyyNkjSDWkOUP3xJpJ+lzGkbNxkVB6bAS9IepKGsyU/ly8ka4EHJH2bYrTKe/WF3s+iTegTEe/UH0TE25L6ZYwnGyeE8rg4dwC2Wk5LP8+qKQu8/Ehb0E7SJhHxNoCkTanoZ2Ml33QZRcQjuWOwVef9LNq0nwF/lXQbRRL/AjAib0h5uFM5M0ljI+IASfMp/mdceopiTfaumUKzFpC0AXAesE1EnCFpJ4olle/OHJo1g6TdKCaBCngwIl7IHFIWTghma4CkWygWJRwaEbtL6gw87rWMrC3xKCOzNWOHiLiMNMkpIj6g+LZp1mY4IZitGf9OtYIAkLQDNaPFzNoCJwSzNeMi4M/A1pJuAh6kWEHTSkxSe0mjc8dRFu5DMFtDJHUHBlE0Ff0tIt7MHJI1g6S7gC95QyMPO82ukdFFDXiUUbk1sp/FrPRzG0nbeD+LNmEB8JykB2g4qfDsfCHl4YSQWURsBCDpEuD/AjdSfMM8GdgoY2jWPPX7WXQCBgDPUvz9+gBPUCyJbeV2T3pUnpuMSkLSExGxz8rKrJwkjQRGRMRz6Xh34NsR8eWsgVmzSOoIfDwdTomIhU1dv65yp3J5LJZ0curkaifpZD5aBtvKb5f6ZAAQEZOBvvnCseaSdDDFhkZXAVcDf5d0UM6YcnENoSQk1QH/TbEvbwDjgG9GxPSMYVkzSbqZov35jxR/v1OADSPipKyB2UpJmgB8MSKmpOOPAzdHRP+8kbU+JwSzNUBSJ+BMil3SAB4FromIBfmisuao3TqzqbIqcEIoCUk9KNbUr6PhFpqnregeM1t9ae+DoBjQAcWAjg4RcWq+qPJwQigJSX8FHqNYD2dp30FEjMoWlDWbpP0pljDfloYJ3ctfl5yk9SmWLT+AYoTYo8DVEVG5meZOCCUhaaIXQmu7JL1EsQ/vsgl9bragzFrI8xDK425Jn46Ie3MHYqtkXkTclzsIs9XhGkJJpBnLXSgWRFuI90NoUyT9CGgP3E7DLVA9U9naDCcEszVA0sPpaf0/qPqEPjhTSNZMkuqWHd4taWBEPJUppGycEEpE0ibAThTLIAAQEY/mi8hWRtJ59U/TzwDmAGMj4tU8UVlLSHoa+GxE/DMdfwL4ZUTskTey1ueZyiUh6SsUoxvuB76ffl6cMyZrlo3SY8P02IhiTaP7JA3JGZg1238Ad0r6mKRPU0wQ/XTmmLJwDaEkJD0HDKRYNrmvpF2A70fEiZlDs1UgaVNgdEQsuxqqlZCkfYFfUax8elREzMkcUhYeZVQeCyJigSQkrR8RL0naOXdQtmoi4i1J3kKzxCT9Lw2Xnt8AmAf8VhIR8bk8keXjhFAeMyRtDNwJPCDpbWBm1ohslUkaDLydOw5r0k9zB1A2bjIqodSp1Q34c0T8O3c8tmKpqW/Zf0SbUiTzoRHxUutHZbZqnBDMVoOkbZcpCmBuRLzX2PVWPpIGAb8AdgU6Uswnea+Kc4DcZGS2GiLiH7ljsNX2S2AI8D8UI8SGUgz/rhwnBDOrvIiYKql9RCwGfp8Wm6wcJ4QSSc0PO0XEaEmdKZbgnZ87LrN13PtpC82Jki4DZlEsI1M5nphWEpK+CtxGMRYaoBfFiCMzW7u+RPFZ+HWKXe+2Bo7LGlEm7lQuCUkTgb2BJyKiXyp7rorT581ak6TPAPdGxJLcseTmGkJ5fFg7xFRSB5Yfzmhma94Q4GVJl0naNXcwOTkhlMcjki4EOkv6FMWIh//NHJPZOi8iTgH6Aa9QdCg/LukMSRtlDq3VucmoJCS1A04HDqNYOfN+4DfhP5BZq5C0GXAK8E3gRWBH4MqI+EXOuFqTE4KZVZqkzwKnATsANwI3RMQbkjYAXoyIZScfrrM87LQkGtmkvX6DFW/SbrZ2nQBcsezeIxHxvqTTMsWUhWsIJeFN2s0sN9cQysObtJtl4LWMPuKEUB4PS/oJ3qTdrLU1tpbRjlkjysQJoTz2ST8H1JQF4E3azdYyr2VUcEIoiYg4JHcMZhXltYwSdypnJumUiPijpPMaOx8Rl7d2TGZVkhaVnE3Rf3AuxeZUV0XEK1kDy8A1hPzqv4lUblakWUnslva1WAB8H0DS1yhmLleKawhmVmmpv+C7EfFQOr4AOCQijswbWetzQshM0pVNnY+Is1srFrMqSktW3A2cDxwB7AIMiYiFWQPLwE1G+U3IHYBZlUXEm5I+B4ym+Pd4fFXXEHMNoWQkdfEG7WZrn6T5NFxiviOwKJVFFSemefnrkpC0r6QXKFZZRNKekq7OHJbZOisiNoqIrjWPThGxYX157vhycEIoj58DhwNzASLiWeCgnAGZWbU4IZRIRLy+TNHiRi80M1sL3KlcHq9L2g+INGvybFLzkZlZa3CnckmkoW//DXySYi+EvwDnePlrM2stTgglIKk9xS5Np+SOxazqJNXXzK+KiF9mDaaVucmoBCJisaQekjpGxL9zx2NWZRGxq6TuwKDcsbQ2J4TymA6Mk3QXsHQeghe3M1u7JP04Iv6fZYovaKRsnedRRuUxk2L6fDuKhe7qH2a2dn2qkbLKrWME7kMoHUkbUcyS/FfuWMzWZZLOBP4PsD0NVzbdCBhXxT49J4SSkLQ7cCOwaSp6ExgaEc/ni8ps3SWpG7AJ8P8Cw2tOzY+It/JElZcTQkmkJXj/MyIeTscHA5dGxH454zKrCkmbA53qjyPitYzhZOE+hPLoUp8MACJiDBXdxs+sNUn6rKSXgVeBRygGeNyXNahMnBDKY5qk70mqS4/vUvwPamZr1w8phpj+PSK2Aw4FxuUNKQ8nhPI4DegB3A7ckZ6fmjUis2pYmFYEaCepXaqp980cUxaeh1ASEfE2xfpFZta63pG0IfAYcJOkNyj2RagcdyqXhKSPA98G6qhJ1BExOFdMZlUgqQuwgGINsZOBbsBNVVxHzAmhJCQ9C1xLsYXf0mWvI8JbbJqtZZK2AAamwycj4o2c8eTihFASkiZERP/ccZhVjaQvAD8BxlDUEg4Ezo+I23LGlYMTQmaS6ieinQ28QdGh/GH9+apOkDFrLal2/qn6WoGkHsDoiNgzb2Stz53K+U2g2NRb6fj8mnNBMa3ezNaedss0Ec2loiMwnRAyS+OezSyfP0u6H7g5HZ8I3JsxnmzcZFQSkk4A/hwR89OktL2AH0TEM5lDM1vnSfo8cABFTf3RiLgjc0hZVLJaVFLfS8ngAOBw4AaKUUdmtvb9lWLZioeAxzPHko0TQnnUDzU9CrgmIv4EdMwYj1klSPoK8CRwLHA88DdJp+WNKg83GZWEpLuBfwKfBPoDH1CMh67cSAez1iRpCrBf/US0tH3mXyNi57yRtT7XEMrjC8D9wBER8Q7FvgjnN3mHma0JM4D5NcfzgdczxZKVawhmVkmSzktP+wJ7AH+iGOp9NEXt/GuZQsvGw07NrKrq9yx/hYZbaP4pQyyl4BqCmZkBriGYWUVJ+l+KJqJGRcTnWjGcUnBCKClJo4GFwFURcXfueMzWQT/NHUDZuMmopCRtCfQEBkXEVbnjMbN1nxOCmVWSpFsj4guSnqORpqOI6JMhrKycEEpC0v7AxcC2FE15AiIivNqp2VogqWdEzJK0bWPnI+IfrR1Tbk4IJSHpJeBclt8xrXLb+JlZHu5ULo95EXFf7iDMqkbSIOAXwK4U64e1B96LiK5ZA8vANYTMJO2Vnn6B4n/E22m4Y9rTOeIyqwpJ44EhwP8AA4ChwI4R8Z9ZA8vANYT8frbM8YCa5wEMbsVYzCopIqZKah8Ri4HfS/pr7phycELILCIOyR2DWcW9L6kjMFHSZcAsoEvmmLLwaqclIelSSRvXHG8i6YcZQzKrii9RfBZ+HXgP2Bo4LmtEmbgPoSQkPRMR/ZYpezoi9lrRPWa2ZknaBNg6IibljiUH1xDKo72k9esPJHUG1m/iejNbAySNkdRV0qbAsxR9CJfnjisHJ4Ty+CPwoKTT0/Z9D1Dsq2xma1e3iHgX+Dzw+4joT7FzYeW4U7kkIuKyNIX+UIpZyj+IiPszh2VWBR0k9aQY+l25oaa1nBBKJE1M8+Q0s9Z1CcX2tWMj4ilJ2wMvZ44pC3cql4RnS5pZbu5DKI9fAidRfDPpDHyFIkGYmbUKNxmViGdLmllOTgjl4dmSZpaVm4zKw7MlzUpA0tGS9skdRw7uVC6RNBltm4iYkjsWs6qSdCmwB9AhIo7MHU9rckIoCUmfpdj0u2NEbCepL3BJRHwub2Rm6zZJ60fEh8uUdYqIBbliysVNRuVxMbA38A5AREwE6rJFY1YdjzdSVskBHe5ULo9FETFPUu44zCpB0seArYDOkvpRrBAA0BXYIFtgGTkhlMdkSV+kWORuJ+BsKvotxayVHA58GegF1C5mNx+4MEdAubkPoSQkbUCxjsphFN9U7qdYz6hy7ZhmrUnScRExKnccZeCEUDKSugIREfNzx2JWFZKOAnoDnerLIuKSfBHl4U7lkpA0MK12Ogl4TtKzkvrnjstsXSfpWuBE4BsUtfMTgG2zBpWJawglIWkScFZEPJaODwCujog+eSMzW7dJmhQRfWp+bgjcHhGH5Y6ttbmGUB7z65MBQESMpejcMrO164P0831JWwILge0yxpONRxllJql+z+QnJf0KuBkIiirsmFxxmVXI3ZI2Bn4CPE3x7+/XWSPKxE1GmUl6uInTERGDWy0Ys4pL+5p3ioh5uWPJwQnBzCpN0nrAmcBBqWgM8KuIWJgtqEycEDKTdApwU6zgDyFpB6Bn6lMwszVM0m+A9YAbUtGXgMUR8ZV8UeXhPoT8ulPsgTABmADMoRgLvSPwCeBNYHi+8MzWeQMjYs+a44ckPZstmoxcQygBSe2BwcD+QE+KUQ8vAvdFxGs5YzNb10l6GjghIl5Jx9sDt0XEXk3fue5xQjCzSpN0KPB7YBrFxLRtgVMjoqkBH+skJwQzq7w0umhnioTw0rL7I1SFE4KZVZqkTsD/AQ6gmIPwGHBtFReWdEIws0qTdCvFqgB/TEUnAZtExAn5osrDCSEzSec1dT4iLm/qvJmtHknPLjPKqNGyKvCw0/w2Sj93BgYCd6XjzwKPZonIrFqekTQoIv4GIGkfYFzmmLJwDaEkJP0FOK5+HwRJGwH/ExFH5I3MbN2UlpsPiklpOwOvpeNtgRciYveM4WXhGkJ5bAP8u+b430BdnlDMKuEzuQMoGyeE8riRYsXTOyi+pRwL/CFvSGbrroj4h6R2wKQq1gYa4yajEkk7pB2QDh+NiGdyxmNWBZJuAr7jVQGcEEolLWGxBTU1N/9ParZ2SXqIYkDHk8B79eUR8blsQWXiJqOSkPQN4CJgNrCYYsZkAN5C02zt+n7uAMrCNYSSkDQV2Cci5uaOxaxqJG0L7BQRoyVtALSvH/FXJd5TuTxeByq5S5NZTpK+CtwG/CoVbQXcmS2gjNxkVB7TgDGS7gGWLqzlmcpma91ZwN7AEwAR8bKkzfOGlIcTQnm8lh4d08PMWseHEfFvSQBI6kDRf1c5TgglERHu2DLL4xFJFwKdJX2KYuXT/80cUxbuVC4JST2AC4DeFFtoAhARg7MFZVYBaXLa6cBhFKP77gd+s6J9ztdlriGUx03ALRTT6b8GDKPYX9nM1q6DgZsi4te5A8nNNYSSkDQhIvpLmhQRfVLZIxHxidyxma3LJP0BGATMpdgc5zFgbES8nTWwDFxDKI+F6ecsSUcBM4FeGeMxq4SIGAogaUvgeOAqYEsq+PlYuTdcYj+U1A34FvALoCvwzawRmVWApFOAA4E9gDeBX1LUEirHTUYlJumbEfHz3HGYrcskvQm8AlwLPBwR0/NGlI8TQolJei0itskdh9m6TlJv4CCK1YZ3AqZExJfyRtX6vHRFuSl3AGbrOkldKTao2pZiU6puwJKcMeXiPoRyc/XNbO0bW/P4ZUTMyBxPNm4yykzSfBr/4BfQOSKctM1aQdrHPCLiX7ljycUJwcwqTdLuFFvYbkrxRWwOMCwiJmcNLAP3IZhZ1V0HnBcR26ZBHN9KZZXjhGBmVdclIh6uP4iIMUCXfOHk4/ZpM6u6aZK+R9FsBHAK8GrGeLJxDcHMqu40oAdwO3BHen5q1ogycaeymRmQlo5ZUsW9lOu5hmBmlSZpoKTngGeB5yQ9K6l/7rhycA3BzCpN0iTgrIh4LB0fAFxdvwx9lbiGYGZVN78+GQBExFigks1GriGYWaVJugLYALiZYtWAE4G3gVEAEfF0vuhalxOCmVWapIebOB1V2tfcCcHMzAD3IZhZRUk6RdIKPwMl7ZA6mCvDM5XNrKq6A89ImgBMoFjUrhOwI/AJiu00h+cLr/W5ycjMKktSe2AwsD/QE/gAeBG4LyJeyxlbDk4IZmYGuA/BzMwSJwQzMwOcEMys4iRt15yyKnBCMLOqG9VI2W2tHkUJeNipmVWSpF2A3kA3SZ+vOdWVYvhp5TghmFlV7Qx8BtgY+GxN+XzgqzkCys0Jwcyq6viI+JKkCyPi0tzBlIH7EMysqvpL2hY4UdImkjatfeQOLgfXEMysqq4F/gxsT7F0hWrORSqvFM9UNrNKk3RNRJyZO44ycEIwMwMkbU7N6KIqrmXkPgQzqzRJn5X0MvAq8AgwHbgva1CZOCGYWdX9EBgE/D0itgMOBcblDSkPJwQzq7qFETEXaCepXUQ8DPTNHFMWHmVkZlX3jqQNgUeBmyS9ASzKHFMW7lQ2s0qT1IViY5x2wMlAN+CmVGuoFCcEM6ustGPa/RHxydyxlIH7EMyssiJiMfC+pG65YykD9yGYWdUtAJ6T9ADwXn1hRJydL6Q8nBDMrOruSY/Kcx+CmVVa6lRekJqP6vsV1o+I9/NG1vrch2BmVfcg0LnmuDMwOlMsWTkhmFnVdYqIf9UfpOcbZIwnGycEM6u69yTtVX8gqT/FvITKcR+CmVWapAHALcDMVNQTODEiJuSLKg+PMjKzykodyAcCu1DssSzgpYhYmDWwTFxDMLNKkzQmIg7OHUcZOCGYWaVJGkGxftEtNJyY9nS2oDJxQjCzSpP0cCPFERGDWz2YzJwQzMwM8LBTM6s4SVtI+q2k+9LxbpJOzx1XDk4IZlZ11wP3A1um478D38wVTE5OCGZWdZtFxK3AEoCIWAQszhtSHk4IZlZ170nqDgSApEHAvLwh5eGJaWZWdecBdwE7SBoH9ABOyBtSHh5lZGaVJml9iiai+pnKU4B2EfFh1sAycEIws0qT9HRE7LWysipwk5GZVZKkjwFbAZ0l9aOoHQB0paLLXzshmFlVHQ58GegFXF5TPh+4MEdAubnJyMwqTdJxETEqdxxl4IRgZpUn6SigN9CpviwiLskXUR6eh2BmlSbpWuBE4BsU/QgnANtmDSoT1xDMrNIkTYqIPjU/NwRuj4jDcsfW2lxDMLOqq98/+X1JWwILge0yxpONRxmZWdXdLWlj4CfA0xRLWPw6a0SZuMnIzCpJ0jeBccAzaUG7+lnLnSLCaxmZmVVIL+C/gV0kTQL+SpEgHs8aVUauIZhZpUnqCAwA9gP2TY93ImK3rIFl4BqCmVVdZ4rlKrqlx0zguawRZeIagplVkqTrKCajzQeeAP4G/C0i3s4aWEYedmpmVbUNsD7wf4F/AjOAd3IGlJtrCGZWWZJEUUvYLz12B94CHo+Ii3LGloMTgplVnqRewP4USeEzQPeI2DhrUBk4IZhZJUk6myIB7E8xO7l+yOk44LmIWJIxvCw8ysjMqqoOuA04NyJmZY6lFFxDMDMzwKOMzMwscUIwMzPACcHWQZI+JmmkpFckvSDpXkkfl1QnaXLu+GpJekLSREmvSZqTnk+UVJc7NqsedyrbOiWNK78DuCEihqSyvsAWwOsZQ2tUROwDIOnLwICI+HreiKzKXEOwdc0hwMKIuLa+ICImRsRjtRdJai/pJ5KekjRJ0n+k8g0lPSjpaUnPSTo6lddJelHSryU9L+kvkjqnc2MkDUjPN5M0PT3/sqQ/SfqzpCmSVjrRSVI7SS9L6lFzPDW97vWSrpX0mKS/S/pMU+/FrKWcEGxdszswoRnXnQ7Mi4iBwEDgq5K2AxYAx0bEXhTJ5Wep1gGwE3BVRPSmWOLguGb8nr2Bk4G+wAn1iWNF0tj3P6Z7AD4JPBsRb6bjOuATwFHAtZI6NfFezFrECcGq6jBgqKSJFAubdaf4wBdwaVoffzSwFUVzE8CrETExPZ9A8eG8Mg9ExNyI+AC4HTigGff8Dhianp8G/L7m3K0RsSQiXgamAbs08V7MWsR9CLaueR44vhnXCfhGRNzfoLBoy+8B9I+Ihan5p1M6/WHNpYsplk0GWMRHX6460dCyE31WOvEnIl6XNFvSYGAfPqotrOj1Gn0vZi3lGoKtax4C1pf01foCSQMlfWKZ6+4HzpS0Xrrm45K6UKyH/0ZKBocA2zbjd04H+qfnyyajT0naNPU3HEOxLEJz/Iai6ejWiFhcU35C6lfYAdgemNLEezFrEScEW6dEMfX+WIoP4lckPQ9cTLHpSa3fAC8AT6ehqL+iqDHfBAyQNJ7im/lLzfi1P6X4QP4rsNky58YCNwITgVERMb6Zb+UuYEMaNhdBkQAeAe4DvhYRC5p4L2Yt4qUrzNaS1RlKmjqfr4iIA2vKrgfujojb1liQZjX8LcKsZCQNB86kYd+B2VrnGoKZmQHuQzAzs8QJwczMACcEMzNLnBDMzAxwQjAzs+T/Bw/3H3LhlqCEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.DataFrame(df.groupby('Cleanup Type')['Gloves & Masks (PPE)'].agg(sum)).plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 'Gloves & Masks (PPE)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20077.000000\n",
       "mean         1.386711\n",
       "std          4.352584\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max        164.000000\n",
       "Name: Gloves & Masks (PPE), dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()['Gloves & Masks (PPE)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the statistics from the describe feature, we the distributions of the features; focusing on the 'Gloves & Masks (PPE)' features specifically, we can see the general distribution of the points. With the average being 2.772180 and the max being 27841.000000, we would like to investigate this further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "ax = df[['Gloves & Masks (PPE)']].plot();\n",
    "\n",
    "ax.set_xlabel('Count');\n",
    "ax.set_ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "ax = df[['Gloves & Masks (PPE)']].boxplot();\n",
    "ax.set_ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these plots of the feature, we can see that there is one distinguishable outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 17,
     "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": [
    "ax = df[df['Gloves & Masks (PPE)'] < 150][['Gloves & Masks (PPE)']].boxplot();\n",
    "ax.set_ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the omission of the Cleanup with a PPE count of 27841, we have produced another box and whisker plot. While there are still notable outliers, we have a clearer view of the distribution (right or positively skewed) and can see that while the median = 0 and the mean = 2.772180, there are still points that are greater than 0 or 1 in the overall distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 18,
     "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": [
    "ax = pd.DataFrame(df['Gloves & Masks (PPE)'].value_counts()).plot(title='Plot of PPE frequencies');\n",
    "ax.set_xlabel('PPE Yield')\n",
    "ax.set_ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot describes the amount of times the frequency of PPE has occurred e.g. How many times did a cleanup yield 0 amounts of PPE. From this plot, we can gather that most Cleanups yielded 0, or no PPE in the collection. This is logical and in accordance with the median = 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Refinement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will be focusing on these features, including the ones constructed:\n",
    "\n",
    "- **'Cleanup Date':** \n",
    "    - the date of the cleanup \n",
    "    - datetime object\n",
    "    - ordinal\n",
    "- **'People'**\n",
    "    - number of people at the cleanup\n",
    "    - float\n",
    "    - discrete nominal\n",
    "- **'Gloves & Masks (PPE)':** \n",
    "    - frequency of PPE in the cleanup \n",
    "    - integer\n",
    "    - discrete nominal\n",
    "- **'State':** \n",
    "    - the state where the cleanup was held \n",
    "    - object or string\n",
    "    - categorical\n",
    "- **'PPE_People'**\n",
    "    - The rate of PPE collected per person\n",
    "    - float\n",
    "    - nominal\n",
    "- **'After_Covid'**\n",
    "    - states whether the cleanup occurred on/after the start of COVID-19\n",
    "    - boolean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Cleanup Date</th>\n",
       "      <th>People</th>\n",
       "      <th>Gloves &amp; Masks (PPE)</th>\n",
       "      <th>PPE_People</th>\n",
       "      <th>State</th>\n",
       "      <th>After_Covid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Michigan, USA</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Texas, USA</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Texas, USA</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Texas, USA</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Washington, USA</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cleanup Date  People  Gloves & Masks (PPE)  PPE_People            State  \\\n",
       "0   2020-06-22     1.0                     0         0.0    Michigan, USA   \n",
       "1   2020-12-24     2.0                     0         0.0       Texas, USA   \n",
       "2   2020-12-24     2.0                     0         0.0       Texas, USA   \n",
       "3   2020-12-24     2.0                     0         0.0       Texas, USA   \n",
       "4   2020-12-27     1.0                     0         0.0  Washington, USA   \n",
       "\n",
       "   After_Covid  \n",
       "0         True  \n",
       "1         True  \n",
       "2         True  \n",
       "3         True  \n",
       "4         True  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PPE_People'] = df['Gloves & Masks (PPE)']/df['People']\n",
    "features = ['Cleanup Date', 'People', 'Gloves & Masks (PPE)', 'PPE_People', 'State', 'After_Covid']\n",
    "data = df[features]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>People</th>\n",
       "      <th>Gloves &amp; Masks (PPE)</th>\n",
       "      <th>PPE_People</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20077.000000</td>\n",
       "      <td>20018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.288987</td>\n",
       "      <td>1.386711</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.834194</td>\n",
       "      <td>4.352584</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1773.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             People  Gloves & Masks (PPE)  PPE_People\n",
       "count  20077.000000          20077.000000     20018.0\n",
       "mean       4.288987              1.386711         inf\n",
       "std       26.834194              4.352584         NaN\n",
       "min        0.000000              0.000000         0.0\n",
       "25%        1.000000              0.000000         0.0\n",
       "50%        1.000000              0.000000         0.0\n",
       "75%        2.000000              1.000000         0.5\n",
       "max     1773.000000            164.000000         inf"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cleanup Date            datetime64[ns]\n",
       "People                         float64\n",
       "Gloves & Masks (PPE)             int64\n",
       "PPE_People                     float64\n",
       "State                           object\n",
       "After_Covid                       bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these statistics, we can see that the average amount of PPE found was ~1.5 and the median was 0: this indicates that while there is PPE, it doesn't seem to constitute a lot of the clean ups. However, we will investigate this phenomenon further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27841"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Gloves & Masks (PPE)'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PPE vs. Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "# Plot for PPE Count as Time progresses\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('PPE Count')\n",
    "plt.plot(data.groupby('Cleanup Date')['Gloves & Masks (PPE)'].apply(sum));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## People vs. Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "# Plot for People as Time progresses\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('People')\n",
    "plt.plot(data.groupby('Cleanup Date')['People'].apply(sum));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PPE/people vs. Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
    "# Plot for PPE Count/Person as Time progresses\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('PPE Count/Person')\n",
    "plt.plot(data.groupby('Cleanup Date')['PPE_People'].apply(sum));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots vs. Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the three plots above, we can see there is an increase in the amount of PPE collected from cleanups. From the second plot, we can see that as time progressed, there are more collectors participating. While it is logical for more PPE to be collected with more volunteers, we can see that the rate of PPE collected per person also increases, similar to the original PPE count per Cleanup. As such, we have reason to believe that there has been an increase in PPE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before and After the Start of COVID-19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will look at the data from before COVID-19 and on/after COVID-19 to gain a better understanding of the dataset and the relationships within it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "before = data.loc[data['After_Covid'] == False]\n",
    "after = data.loc[data['After_Covid']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "# Plot to compare the masks from before COVID-19 and after\n",
    "ax = pd.DataFrame(data.groupby('After_Covid')['Gloves & Masks (PPE)'].agg(sum)).plot.bar();\n",
    "ax.set_ylabel('Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot indicates that there is a possible correlation between COVID-19 and PPE waste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "plt.xlabel('Date (Before COVID-19)')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.ylabel('PPE count')\n",
    "plt.plot(before.groupby('Cleanup Date')['Gloves & Masks (PPE)'].apply(sum));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "plt.xlabel('Date (After COVID-19)')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.ylabel('PPE count')\n",
    "plt.plot(after.groupby('Cleanup Date')['Gloves & Masks (PPE)'].apply(sum));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By refining the granularity to before and after the declaration of the COVID-19 pandemic, we are able to see the general trends that were occuring in these specified time periods. For instance, we can see that generally, there were no PPE collected, with only an occurence of one item. Compared to the second plot post-COVID-19, we can see that there are several more spikes, one even going farther than 4,000. As such, it is very possible that the COVID-19 measures could be related to the trends of PPE counts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## t-test\n",
    "\n",
    "Here, we have conducted a t-test, with the significance level = 0.01, to compare the distribution of points before and after the start of COVID-19; however, because of the limitations of the size of our dataset (especially of the points prior), we will take this into consideration when evaluating results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(468,)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before['PPE_People'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19609,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "after['PPE_People'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is no significant difference, and we cannot reject the null hypothesis.\n"
     ]
    }
   ],
   "source": [
    "t_val = ttest_ind(before['PPE_People'], after['PPE_People']).statistic\n",
    "p_val = ttest_ind(before['PPE_People'], after['PPE_People']).pvalue\n",
    "\n",
    "assert t_val\n",
    "assert p_val\n",
    "\n",
    "if p_val < 0.01: print('We can reject the null hypothesis and potentially consider our alternative hypothesis.')\n",
    "else: print('There is no significant difference, and we cannot reject the null hypothesis.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This test determined that there is no significant difference between the distribution of rates of PPE collected before and after the start of the COVID-19 pandemic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Gloves &amp; Masks (PPE)</th>\n",
       "      <th>PPE_People</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California, USA</th>\n",
       "      <td>16833</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida, USA</th>\n",
       "      <td>4020</td>\n",
       "      <td>2052.613427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York, USA</th>\n",
       "      <td>1350</td>\n",
       "      <td>999.575880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut, USA</th>\n",
       "      <td>643</td>\n",
       "      <td>317.159524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts, USA</th>\n",
       "      <td>571</td>\n",
       "      <td>294.492861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia, USA</th>\n",
       "      <td>552</td>\n",
       "      <td>264.401348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina, USA</th>\n",
       "      <td>429</td>\n",
       "      <td>157.141098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio, USA</th>\n",
       "      <td>405</td>\n",
       "      <td>158.975914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas, USA</th>\n",
       "      <td>397</td>\n",
       "      <td>183.887731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington, USA</th>\n",
       "      <td>334</td>\n",
       "      <td>254.605556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Gloves & Masks (PPE)   PPE_People\n",
       "State                                                 \n",
       "California, USA                     16833          inf\n",
       "Florida, USA                         4020  2052.613427\n",
       "New York, USA                        1350   999.575880\n",
       "Connecticut, USA                      643   317.159524\n",
       "Massachusetts, USA                    571   294.492861\n",
       "Virginia, USA                         552   264.401348\n",
       "North Carolina, USA                   429   157.141098\n",
       "Ohio, USA                             405   158.975914\n",
       "Texas, USA                            397   183.887731\n",
       "Washington, USA                       334   254.605556"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have grouped the data by States, and we noticed that many masks were found on coasts.\n",
    "by_state = data.groupby('State')[['Gloves & Masks (PPE)', 'PPE_People']].agg(sum)\n",
    "top_10 = by_state.sort_values(by='Gloves & Masks (PPE)', ascending=False).iloc[:10]\n",
    "top_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "# Bar plot of PPE Count from each of the top 10 US states\n",
    "plt.xlabel('State')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.ylabel('PPE Count')\n",
    "plt.bar(top_10.index, top_10['Gloves & Masks (PPE)']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction\n",
    "\n",
    "We would like to construct a prediction model to better understand the trends between the amount of PPE in our oceans and COVID-19 regulations. For our model, we will be incorporating all the features into a Logistic Regression model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constructing the Predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have conducted a predictor function that takes an input of the predictor features, the prediction value, and the model of choice (defaulting to Logistic Regression). It first uses sklearn's train_test_split to construct a set of three training/validation/test data sets each 50%/25%/25% respectfully. This is one measure to prevent overfitting. Next, it fits the training set to the model of choice, then predicting on the validation and test data sets. The function will print and output the calculated accuracies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictor(X, y, mod=LogisticRegression()):\n",
    "    '''Constructs a predictor (default: Logistic Regression) \n",
    "    and outputs the accuracies on validation and test sets\n",
    "    as print statements'''\n",
    "    \n",
    "    # first, split to make test sets\n",
    "    X_, X_test, y_, y_test = train_test_split(X, y, test_size=0.25)\n",
    "    \n",
    "    # split remaining into training and validation sets\n",
    "    X_train, X_valid, y_train, y_valid = train_test_split(X_, y_, test_size=0.50)\n",
    "\n",
    "    # train a logistic regression model and fit to training set\n",
    "    model = mod.fit(X_train, y_train)\n",
    "\n",
    "    # predict with validation and test set\n",
    "    valid_preds = mod.predict(X_valid) \n",
    "    test_preds = mod.predict(X_test) \n",
    "\n",
    "    # calculate accuracy\n",
    "    valid_acc = accuracy_score(valid_preds, y_valid)\n",
    "    test_acc = accuracy_score(test_preds, y_test)\n",
    "\n",
    "    print('validation accuracy: ' + str(valid_acc))\n",
    "    print('test accuracy: ' + str(test_acc))\n",
    "\n",
    "    return valid_acc, test_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing the Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These features were chosen based off of our previous EDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cleanup Date</th>\n",
       "      <th>People</th>\n",
       "      <th>State</th>\n",
       "      <th>After_Covid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Michigan, USA</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>2020-12-24</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>Texas, USA</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Washington, USA</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cleanup Date  People            State  After_Covid\n",
       "0   2020-06-22     1.0    Michigan, USA         True\n",
       "1   2020-12-24     2.0       Texas, USA         True\n",
       "2   2020-12-24     2.0       Texas, USA         True\n",
       "3   2020-12-24     2.0       Texas, USA         True\n",
       "4   2020-12-27     1.0  Washington, USA         True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor_vals = data[['Cleanup Date', 'People', 'State', 'After_Covid']]\n",
    "predictor_vals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20077, 4)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor_vals.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'Cleanup Date' is an ordinal feature, so we will transform this feature with the Ordinal Encoder from sklearn. Evidently, the 'State' feature consists of the names of United States states, which is a categorical feature represented as a string. Before feeding this feature into our predictor, we will also one hot encode it using sklearn's preprocessing method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = OrdinalEncoder().fit_transform(predictor_vals[['Cleanup Date']])\n",
    "states = OneHotEncoder().fit_transform(predictor_vals[['State']]).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will set our predictor value to be the 'Gloves & Masks (PPE)' feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Gloves & Masks (PPE), dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data['Gloves & Masks (PPE)']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting with the Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we tried out a model with only the ordinal encoded 'Cleanup Date', and found that the accuracy on the test set was at about"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation accuracy: 0.71682826404569\n",
      "test accuracy: 0.7127490039840637\n"
     ]
    }
   ],
   "source": [
    "# dates only\n",
    "dates_valid, dates_test = predictor(dates, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation accuracy: 0.7158985257006243\n",
      "test accuracy: 0.7119521912350597\n"
     ]
    }
   ],
   "source": [
    "# states only\n",
    "states_valid, states_test = predictor(states, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation accuracy: 0.7054057643777394\n",
      "test accuracy: 0.7145418326693227\n"
     ]
    }
   ],
   "source": [
    "# (number of) people only\n",
    "people_valid, people_test = predictor(predictor_vals[['People']], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation accuracy: 0.714570328064816\n",
      "test accuracy: 0.7121513944223108\n"
     ]
    }
   ],
   "source": [
    "# (if occurred) after covid only \n",
    "covid_valid, covid_test = predictor(predictor_vals[['After_Covid']], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "     0    1    2    3    4    5    6    7    8    9  ...   35   36   37   38  \\\n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
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       "\n",
       "    39   40   41   Date  People  After_Covid  \n",
       "0  0.0  0.0  0.0  131.0     1.0         True  \n",
       "1  0.0  0.0  0.0  316.0     2.0         True  \n",
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       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.DataFrame(states) # one hot encoded 'State'\n",
    "X['Date'] = pd.DataFrame(dates)[0] # ordinal encoded 'Cleanup Dates'\n",
    "X['People'] = predictor_vals['People'] # 'People'\n",
    "X['After_Covid'] = predictor_vals['After_Covid'] # 'After_Covid'\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation accuracy: 0.7088590782308407\n",
      "test accuracy: 0.7183266932270916\n"
     ]
    }
   ],
   "source": [
    "all_valid, all_test = predictor(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results \n",
    "\n",
    "After calulating the validation and test accuracies of the features, we have produced the following results. There is not much variation between the accuracies, and we believe this may be attributed to the smaller size of our dataset, especially considering that it mostly encompasses the period of COVID-19 and focuses on only the US specifically. With more hyperparameter tuning, potentially another dataset, and other models, we believe this predictor could be further optimized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
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       "      <th>After_Covid</th>\n",
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       "  <tbody>\n",
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       "      <th>validation</th>\n",
       "      <td>0.716828</td>\n",
       "      <td>0.715899</td>\n",
       "      <td>0.705406</td>\n",
       "      <td>0.714570</td>\n",
       "      <td>0.708859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>0.712749</td>\n",
       "      <td>0.711952</td>\n",
       "      <td>0.714542</td>\n",
       "      <td>0.712151</td>\n",
       "      <td>0.718327</td>\n",
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      ],
      "text/plain": [
       "                Date     State    People  After_Covid       All\n",
       "Accuracy                                                       \n",
       "validation  0.716828  0.715899  0.705406     0.714570  0.708859\n",
       "test        0.712749  0.711952  0.714542     0.712151  0.718327"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({'Date': [dates_valid, dates_test], 'State':[states_valid, states_test], \n",
    "            'People': [people_valid, people_test], 'After_Covid': [covid_valid, covid_test], \n",
    "             'All': [all_valid, all_test], 'Accuracy': ['validation', 'test']}).set_index('Accuracy')\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   Platform availability is a big concern as well for organizations or researchers who have worked hard to collect accurate plastic pollutant data, but experience limitations when it comes to sharing the data with the world. Additionally, data may not be compiled equitably around the world considering how certain oceanic locations differ from others.\n",
    "   \n",
    "   Since our primary source of pollutant data was the Coastal Cleanup Project, one potential issue is how developed and accessible a region’s coastline is (for instance, California and Florida both have very accessible coastlines). Also, there are drastically more coastlines in oceanic regions than those limited to lakes or rivers. Socioeconomic factors may limit parts of the population from participating in volunteer events, so data reporting may be restricted in certain regions. However, we implement a control by looking for PPE collected per capita at volunteer events we are able to find trends.\n",
    "   \n",
    "   Additionally, our dataset is contained within February 20, 2020 to February 20, 2021-- the amount of data prior to COVID-19 is significantly less than that during/after the pandemic. This study focused on data from the United States, so this data may not hold true for other countries with different levels of wealth, education, resources, and cultures. For instance, in areas that are notoriously polluted, there may be more resources already allocated to coastal cleanup unrelated to The Coastal Cleanup Project. We will note these biases throughout our project and appropriately discuss their potential impacts on our visualizations and results. \n",
    "   \n",
    "   \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   With our plots, we tried to understand how the COVID-19 pandemic could be correlated to the amount of Preventative Protection Equipment (PPE) waste found in our oceans. Using our dataset, we considered: the cleanup date, amount of people participating in the cleanup, amount of PPE collected, amount of PPE collected per person, the state the cleanup was located, and whether the cleanup occurred before or after the COVID-19 pandemic. When evaluating our results, we will also take into consideration that our dataset spans one year, 02-20-20 to 02-20-21. As such, there will be fewer data to analyze the trends prior to COVID-19. Additionally, the PPE feature consists of both Gloves and Masks and includes data only on the United States. With each of these factors, we were able to gain a better idea about this relationship. \n",
    "\n",
    "   Plotting the _PPE counts per Cleanup Date_ immediately displayed a large spike in PPE counts. Additionally, we plotted the amount of people participating _('People') per Cleanup Date_; this plot did show an increase in number of volunteers, which could be a confounding factor as to why there was more PPE collected. However, when plotting the _amount of PPE collected per person_, we could see that this plot mirrored the original _PPE counts per Cleanup Date_: this exemplifies how the rise in PPE could be attributed to something else other than number of volunteers, potentially the COVID-19 regulations. Next, we refined our granularity to before and after the declaration of COVID-19, which was March 11, 2020 (03/11/21). We performed a t-test on these sets, finding that the p-value was less than our set significance level of 0.01, so they were not in fact significantly different and we cannot reject the null hypothesis; this was to be expected given our dataset sizes (468 vs. 19609). When observing the cleanups per U.S. state, we can see that there was more PPE collected in states near coastlines, which makes sense logically. From this refinement, we can see that the states with the most PPE accumulated were California, Florida, New York, Connecticut, and Massachusetts. We believe this ordering also makes sense, because these states are both near coasts and densely populated.\n",
    "\n",
    "   For our prediction task, we focused on predicting the amount of PPE, 'Gloves & Masks (PPE)' collected given the features 'Cleanup Date', 'People', 'State', and 'After_Covid'. We defined a function to first split the data into training/validation/test datasets and to fit and predict with a Logistic Regression model. We found that given each of the features individually and on all the features together, the validation and test accuracies amounted to about 70%-71%. We believe that these results are fair given the limitations on the size of our dataset. If we were to further refine our model (_more data_, data engineering, hypertuning, other models), we believe that this predictor would be beneficial in forecasting how our environment will appear in the years to come. Additionally, this project will bring to light how COVID-19 has affected our communities beyond the people it infects and will bring greater awareness to climate change.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Sikandar Bangash\n",
    "- Ian Jeffers\n",
    "- Mary Mei Longano\n",
    "- Rachel Ung: Overview, Data Cleaning, Data Analysis & Results, Prediction, Discussion & Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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