FinalProject_group127.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J3X7zg2pGFvL"
   },
   "source": [
    "# COGS 108 - Final Project: Exploring Happiness in the United States"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QJI87sV0GImD"
   },
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6AbC--UQGItW"
   },
   "source": [
    "For this project we were aiming to analyze the different factors that affect happiness in the US. We wanted to determine if happiness can be measured reasonably well using just 5 parameters: \n",
    "\n",
    "1) GDP per capita\n",
    "2) Life expectancy\n",
    "3) Employment\n",
    "4) Social progress\n",
    "5) Gini coefficient (income distribution)\n",
    "\n",
    "We obtained data for each parameter using web scraping and data wrangling. Next, we used simple linear regression to compare each parameters with the WalletHub happiness index, which was used as a reference point. Using the correlation coefficient (R^2) for each parameter, we were able to find a weighted formula A * GDP + B * Life_Expectancy + C * Employment + D * Social_Progress + E * Gini_Coefficient, where the weights A,B,C,D,E are the r^2 values for each parameter. Finally, we tested the model against the Happy State Index (HSI), in order to determine how close/far our model was from the HSI. In the end, we got a result of r^2 = 0.65, which indicates a relatively well fitting model, with some discrepancies. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xm__DwcoGI1E"
   },
   "source": [
    "# Names\n",
    "\n",
    "- Max Kazakov\n",
    "- Shrimant Singh\n",
    "- Egbert Doan\n",
    "- Eric Salguero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SEA15QFLGF7o"
   },
   "source": [
    "# Group Members IDs\n",
    "\n",
    "- AXXXXXXXX\n",
    "- AXXXXXXXX\n",
    "- AXXXXXXXX\n",
    "- AXXXXXXXX\n",
    "\n",
    "(respectively)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XxiR5aNiGGGV"
   },
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Qkhyn66rGE_I"
   },
   "source": [
    "Our research question can be summarized as follows: \n",
    "\n",
    "### \"Are 5 parameters (GDP per capita, life expectancy, employment, social progress, and Gini coefficient (income distribution)) sufficient enough to produce a *reasonable* model of happiness across the 50 United States of America?\" \n",
    "\n",
    "Please note that in our view a *reasonable* model is one that has an r^2 value >= 75. One may argue that this cut-off level is too low, however, for the purpose of the present analysis, we deemed it adequate, since we are looking to find a compromise between the model's simplicity and predictive capacity. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ULVI9kGZGySF"
   },
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Xu5idVUrGyei"
   },
   "source": [
    "For background work we started out looking at the [World Happiness Index](https://worldhappiness.report/), however we realized that for the purpose of narrowing down our research project it would be better to limit ourselves to the US. Essentially, we attempted to recreate a country-by-country comparison but on a state-by-state level. However, we explored the methodology of the world happiness report, which inspired us to use some of the parameters, e.g. GDP per capita. \n",
    "\n",
    "In terms of existing work on the subject matter, we were able to find this [report](https://wallethub.com/edu/happiest-states/6959/) by WalletHub. Below is an excerpt from their methodology.  \n",
    "\n",
    "\"In this study, WalletHub drew upon the findings of “happiness” research to determine which environmental factors are linked to a person’s overall well-being and satisfaction with life. Previous studies have found that good economic, emotional, physical and social health are all key to a well-balanced and fulfilled life.\n",
    "\n",
    "To determine where Americans exhibit the best combination of these factors, we examined the 50 states across 31 key metrics, ranging from depression rate to sports participation rate to income growth. Read on for our findings, additional insight from a panel of experts and a full description of our methodology.\"\n",
    "\n",
    "What is key here is that WalletHub used 31 metrics across three main dimensions:\n",
    "\n",
    "1) Emotional & Physical Well-Being\n",
    "2) Work Environment \n",
    "3) Community & Environment\n",
    "\n",
    "Our goal was to come up with a Happiness Index that would only use 5 metrics, roughly 6 times simpler than WalletHub's. However, we cross-referenced WalletHub's index along the way in order to determine the coefficients for our model, as well as in the very end when verifying our end result.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GQozI7_DGys0"
   },
   "source": [
    "# Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "39CtTqY2G-Ac"
   },
   "source": [
    "Our hypothesis was that *a model consisting of 5 metrics can produce an r^2 value of 0.75 or higher*. Therefore we expect a positive answer to the research question. Namely, that the five selected metrics (GDP per capita, life expectancy, employment, social progress, and Gini coefficient (income distribution)) are enough to produce a model that yields an r^2>=75, when a simple linear regression is performed, with the Happy State Index as the dependent variable.\n",
    "\n",
    "The reason why we believe that our model can perform this well is due to the fact that the selected parameters cover a wide range of factors affecting one's perception of happiness. Moreover, the 5 parameters were not selected at random, instead they were generated through both, our common-sense intuition, and background research of other happiness indices (of global and national scale). \n",
    "\n",
    "The opposite outcome would be if the model fails to reach the 0.75 threshold. That would mean that either 5 parameters do not sufficiently explain the state of happiness in the US, or that our model is overly simple. The latter would merit a reevaluation of the model and better fine-tuning of the coefficients in possible future iterations of the research project. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VWtDQoPiG-Jf"
   },
   "source": [
    "# Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tN-fL6NqG-PI"
   },
   "source": [
    "- Dataset Name: List of U.S. states by GDP per capita\n",
    "- Link to the dataset: https://en.wikipedia.org/wiki/List_of_U.S._states_by_GDP_per_capita\n",
    "- Number of observations: 51\n",
    "\n",
    "This dataset shows the GDP per capita of the 50 U.S. states and also includes the District of Columbia. For our observations we have omitted the District of Columbia to only observe the 50 U.S. states.\n",
    "\n",
    "This dataset will be used along side four other datasets to create a weighted formula for linear regression. This will allow us to see what is affecting happiness in the US. GDP per capita should come to affect happiness in a state as this represents the states economic growth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5uTsiwFLG-TU"
   },
   "source": [
    "- Dataset Name: Happiest States in the U.S.\n",
    "- Link to the dataset: https://wallethub.com/edu/happiest-states/6959/\n",
    "- Number of observations: 50\n",
    "\n",
    "This dataset shows the Happines Index used by wallethub of the 50 U.S. states. Using this metric we will define our own index and compare.\n",
    "\n",
    "This dataset will be compared to our linear regression model to see how accurate our model is with the factors and weights we have calculates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zvm4I-xZG-a8"
   },
   "source": [
    "- Dataset Name: List of U.S. states and territories by life expectancy\n",
    "- Link to the dataset: https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_life_expectancy\n",
    "- Number of observations: 56\n",
    "\n",
    "This dataset shows the  life expectancy of the 50 U.S. states and also includes U.S. territories. For our observations we have omitted the U.S. terrritores to only observe the 50 U.S. states.\n",
    "\n",
    "This dataset will be used along side four other datasets to create a weighted formula for linear regression. We believe that Life expectancy will be a factor in happiness as having a longer life can be said improve happiness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vt2movYHHZSf"
   },
   "source": [
    "- Dataset Name: Unemployment Rates for States, 2018 Annual Averages\n",
    "- Link to the dataset: https://www.bls.gov/lau/lastrk18.htm\n",
    "- Number of observations: 50\n",
    "\n",
    "This dataset shows the Unemployment Rates of the 50 U.S. states. We will use the 100 - unemployment from this dataset to get values for the employment rate in the U.S.\n",
    "\n",
    "This dataset will be used along side four other datasets to create a weighted formula for linear regression. Using 100 - unemployment will give us a look at employment rates as a factor in our linear regression model. Both unemployment rates and employment rates are similarly correlated but unemployment is negatively correlated rather than employment which is positive correlation for our model, since it is a sum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aiZPvrAfHZXz"
   },
   "source": [
    "- Dataset Name: US Social Progress Index Results Public\n",
    "- Link to the dataset: https://socialprogress.blog/2018-social-progress-index-us-states/\n",
    "- Number of observations: 52\n",
    "\n",
    "This dataset shows another index that takes into account social aspects like crime rates, medical care and other quality of life factors.\n",
    "\n",
    "This dataset will be used along side four other datasets to create a weighted formula for linear regression. This index takes into account a variety of social aspects that can weigh on happiness. Aspects like clean water and broadband connection availablity are included in this index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zy1ePXf-HZiU"
   },
   "source": [
    "- Dataset Name: List of U.S. states by Gini coefficient\n",
    "- Link to the dataset: https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient\n",
    "- Number of observations: 52\n",
    "\n",
    "The gini coefficient measures the income gap in a state comparing the low income class with the high income class. This dataset shows the Gini coefficient of the 50 U.S. states and also includes the District of Columbia and the U.S. of America. For our observations we have omitted the District of Columbia and and the U.S. of America to only observe the 50 U.S. states.\n",
    "\n",
    "This dataset will be used along side four other datasets to create a weighted formula for linear regression. This data set looks at the income gap which can affect happiness for low income class negatively and high income class positively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-WIbCIp5D-eW"
   },
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OaromlMkEuta"
   },
   "source": [
    "### Data Cleaning & Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apart from the standard data science toolkit, sklearn was used for Linear Regression models, and seaborn was used for better visualizations. Plotly was used in offline mode for choropleth plots of geospatial data. Patsy and the statsmodels API was used in order to carry out the final OLS testing and determine the r^2 value between our model and the Happy State Index. Scipy was used for descriptive statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_wKROch2D-eX"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import bs4\n",
    "from bs4 import BeautifulSoup\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import seaborn as sns\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Av9H16isD-ea"
   },
   "source": [
    "Initiating Plotly notebook mode for offline Plotly plots (used for Data Visualization with Choropleth plots)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "colab_type": "code",
    "id": "tsnZqkw6D-ea",
    "outputId": "9c733aa6-1075-41e7-9bd1-63df1a877e37"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G04FyTlaD-ee"
   },
   "source": [
    "Set seaborn aesthetic parameters for notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zymKaEspD-ef"
   },
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hfaMjdfND-eg"
   },
   "source": [
    "## Web scrapping and data wrangling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7LtBqISBD-eh"
   },
   "source": [
    "### 1. GDP per capita 2018\n",
    "\n",
    "For GDP, the wikipedia page was scraped without much data cleaning being required, since wikipedia data is usually fairly clean as-is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iSYPNNbuD-eh"
   },
   "outputs": [],
   "source": [
    "wiki = 'https://en.wikipedia.org/wiki/List_of_U.S._states_by_GDP_per_capita'\n",
    "page = requests.get(wiki)\n",
    "soup = BeautifulSoup(page.content, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aSvRBUJkD-ej"
   },
   "outputs": [],
   "source": [
    "title_page = soup.title.string\n",
    "assert 'GDP' in title_page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xDM1L7lzD-el"
   },
   "outputs": [],
   "source": [
    "right_table = soup.find(\"table\", class_='wikitable sortable')\n",
    "assert isinstance(right_table, bs4.element.Tag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eBKU4445D-em"
   },
   "outputs": [],
   "source": [
    "states, GDP = [], []\n",
    "\n",
    "for row in right_table.findAll('tr'):\n",
    "    \n",
    "    cells = row.findAll('td')\n",
    "    \n",
    "\n",
    "    if len(cells) == 0 or '—' in cells[0]:\n",
    "        continue\n",
    "\n",
    "    states.append(cells[1].find('a').text)\n",
    "    GDP.append(cells[2].find(text=True).replace('\\n', ''))\n",
    "    \n",
    "assert len(states) == 50\n",
    "assert len(GDP) == 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qRuvPIL3D-ep"
   },
   "outputs": [],
   "source": [
    "GDP_df = pd.DataFrame(list(zip(states, GDP)), \n",
    "            columns = ['State', 'GDP 2018'])\n",
    "\n",
    "GDP_df.set_index('State', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hY9aJg7XD-er"
   },
   "source": [
    "### Happy State Index\n",
    "\n",
    "Similarly to the Wikipedia web scraping, the Happy State Index was fairly clean. However, a small problem was encountered along the way, namely the website would block our IP after multiple requests. We had to use a VPN in order to circumvent this problem (the same is recommended for anyone interested in replicating our results). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZhiXC6ofD-er"
   },
   "outputs": [],
   "source": [
    "whub = 'https://wallethub.com/edu/happiest-states/6959/'\n",
    "page = requests.get(whub)\n",
    "soup = BeautifulSoup(page.content, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "PVuT_4nyD-eu",
    "outputId": "a29a320b-b882-4281-e073-3b6194ae9f01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Happiest States in America'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_page = soup.title.string\n",
    "title_page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6kMlTA9TD-ew"
   },
   "outputs": [],
   "source": [
    "right_table = soup.find(\"table\", class_='cardhub-edu-table center-aligned sortable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5yjtn5oeD-ey"
   },
   "outputs": [],
   "source": [
    "states, HSI = [], []\n",
    "\n",
    "for row in right_table.findAll('tr'):\n",
    "    \n",
    "    cells = row.findAll('td')\n",
    "    \n",
    "\n",
    "    if len(cells) == 0:\n",
    "        continue\n",
    "    \n",
    "\n",
    "    states.append(cells[1].find(text=True))\n",
    "    HSI.append(cells[2].find(text=True))\n",
    "    \n",
    "assert len(states) == 50\n",
    "assert len(HSI) == 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9u2FHUi8D-ez"
   },
   "outputs": [],
   "source": [
    "HSI_df = pd.DataFrame(list(zip(states, HSI)), \n",
    "            columns = ['State', 'Happy State Index'])\n",
    "\n",
    "HSI_df.set_index('State', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "34vfjmiFD-e1"
   },
   "outputs": [],
   "source": [
    "df = pd.merge(HSI_df, GDP_df, on='State', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NiwCyYAZD-e2"
   },
   "outputs": [],
   "source": [
    "df['GDP 2018'] = df['GDP 2018'].str.replace(',', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data needs to be converted to float. Additionally, GDP was devided by 1000 and rounded to 2 decimal places using an anonymous function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aiuJ-NUoD-e4"
   },
   "outputs": [],
   "source": [
    "df['Happy State Index'] = df['Happy State Index'].astype(float)\n",
    "df['GDP 2018'] = df['GDP 2018'].apply(lambda x : round(int(x) / 1000, 2)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d0uZQa6VD-e6"
   },
   "source": [
    "### 2. Life Expectancy\n",
    "\n",
    "The procedure for life expectancy was slightly more complicated than the rest of the Wikipedia web scraping due to some of the hyperlinks containing \"(state)\" and other unnecessary substrings. This was basically the only data cleaning that was carried out. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_EFUwfHcD-e6"
   },
   "outputs": [],
   "source": [
    "wiki = 'https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_life_expectancy'\n",
    "page = requests.get(wiki)\n",
    "soup = BeautifulSoup(page.content, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FY1GwqZjD-e9"
   },
   "outputs": [],
   "source": [
    "title_page = soup.title.string\n",
    "assert 'life' in title_page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8kV38PWBD-fA"
   },
   "outputs": [],
   "source": [
    "right_table = soup.find(\"table\", class_='wikitable sortable')\n",
    "assert isinstance(right_table, bs4.element.Tag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_BT3dCBgD-fC"
   },
   "outputs": [],
   "source": [
    "states, LE = [], []\n",
    "\n",
    "for row in right_table.findAll('tr'):\n",
    "    \n",
    "    cells = row.findAll('td')\n",
    "    \n",
    "    if len(cells) == 0:\n",
    "        continue\n",
    "        \n",
    "    # Special handling for Georgia \n",
    "    if cells[1].find('a') != None:\n",
    "        st = cells[1].find('a')['title']\n",
    "        states.append(st)\n",
    "        LE.append(cells[2].find(text=True).replace('\\n', ''))\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U6fY7VSmD-fE"
   },
   "outputs": [],
   "source": [
    "LE_df = pd.DataFrame(list(zip(states, LE)), \n",
    "            columns = ['State', 'Life Expectancy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anonymous functions were used to clean the entries that contained redundant substrings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hc0cJ3CMD-fG"
   },
   "outputs": [],
   "source": [
    "LE_df['State'] = LE_df['State'].apply(lambda x: x.replace(' (state)', ''))\n",
    "LE_df['State'] = LE_df['State'].apply(lambda x: x.replace(' (U.S. state)', ''))\n",
    "LE_df.set_index('State', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The wikipedia dataset contained U.S. territories as well, which had to be filtered out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cnR6n08KD-fI"
   },
   "outputs": [],
   "source": [
    "LE_df = LE_df[LE_df.index.isin(df.index.tolist())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EeZRttZID-fJ"
   },
   "outputs": [],
   "source": [
    "df = pd.merge(df, LE_df, on='State', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kESrRiMLD-fL"
   },
   "outputs": [],
   "source": [
    "df['Life Expectancy'] = df['Life Expectancy'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "pbnF26QkD-fN",
    "outputId": "e62847a9-f71c-44b5-b23d-7491de9f530c"
   },
   "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>Happy State Index</th>\n",
       "      <th>GDP 2018</th>\n",
       "      <th>Life Expectancy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>68.27</td>\n",
       "      <td>51.28</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>67.84</td>\n",
       "      <td>44.64</td>\n",
       "      <td>80.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>67.26</td>\n",
       "      <td>53.70</td>\n",
       "      <td>81.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>65.62</td>\n",
       "      <td>62.84</td>\n",
       "      <td>79.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>63.14</td>\n",
       "      <td>53.99</td>\n",
       "      <td>80.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Happy State Index  GDP 2018  Life Expectancy\n",
       "State                                                     \n",
       "Hawaii                    68.27     51.28             81.3\n",
       "Utah                      67.84     44.64             80.2\n",
       "Minnesota                 67.26     53.70             81.1\n",
       "North Dakota              65.62     62.84             79.5\n",
       "California                63.14     53.99             80.8"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IvDhkKXmD-fP"
   },
   "source": [
    "### 3. Employment Rate\n",
    "\n",
    "For employment rate we had to use data from the Bureau of Labor Statistics, web scraping was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s83HCxnQD-fP"
   },
   "outputs": [],
   "source": [
    "url = 'https://www.bls.gov/lau/lastrk18.htm'\n",
    "page = requests.get(url)\n",
    "soup = BeautifulSoup(page.content, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f188Hup2D-fR"
   },
   "outputs": [],
   "source": [
    "right_table = soup.find(id='lastrk18')\n",
    "assert isinstance(right_table, bs4.element.Tag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xPaR5wcnD-fS"
   },
   "outputs": [],
   "source": [
    "states, EMP = [], []\n",
    "\n",
    "for row in right_table.findAll('tr'):\n",
    "\n",
    "    th = row.findAll('th')\n",
    "    td = row.findAll('td')\n",
    "    \n",
    "    if len(th) == 0 or len(td) == 0:\n",
    "        continue\n",
    "        \n",
    "    states.append(th[0].find(text=True))\n",
    "    EMP.append(td[0].find(text=True))\n",
    "    \n",
    "assert len(states) == 52"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zoL4nkVzD-fU"
   },
   "outputs": [],
   "source": [
    "EMP_df = pd.DataFrame(list(zip(states, EMP)), \n",
    "            columns = ['State', 'Employment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PaXP6CI2D-fW"
   },
   "outputs": [],
   "source": [
    "EMP_df.set_index('State', inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"District of Columbia\" had to be filtered out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "THkVGmeCD-fY"
   },
   "outputs": [],
   "source": [
    "EMP_df = EMP_df[EMP_df.index.isin(df.index.tolist())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GNrt0vxND-fZ"
   },
   "outputs": [],
   "source": [
    "df = pd.merge(df, EMP_df, on='State', how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple arithmetic conversion was used to get from employment % to unemployment %."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "yUw-Tu7iD-fb",
    "outputId": "764a51a5-b26f-42d7-c1e2-f21ab3ea08b1"
   },
   "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>Happy State Index</th>\n",
       "      <th>GDP 2018</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Employment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>68.27</td>\n",
       "      <td>51.28</td>\n",
       "      <td>81.3</td>\n",
       "      <td>97.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>67.84</td>\n",
       "      <td>44.64</td>\n",
       "      <td>80.2</td>\n",
       "      <td>96.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>67.26</td>\n",
       "      <td>53.70</td>\n",
       "      <td>81.1</td>\n",
       "      <td>97.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>65.62</td>\n",
       "      <td>62.84</td>\n",
       "      <td>79.5</td>\n",
       "      <td>97.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>63.14</td>\n",
       "      <td>53.99</td>\n",
       "      <td>80.8</td>\n",
       "      <td>95.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Happy State Index  GDP 2018  Life Expectancy  Employment\n",
       "State                                                                 \n",
       "Hawaii                    68.27     51.28             81.3        97.6\n",
       "Utah                      67.84     44.64             80.2        96.9\n",
       "Minnesota                 67.26     53.70             81.1        97.1\n",
       "North Dakota              65.62     62.84             79.5        97.4\n",
       "California                63.14     53.99             80.8        95.8"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Employment'] = 100 - df['Employment'].astype(float)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UiuKhKGTD-ff"
   },
   "source": [
    "### 4. Social Progress\n",
    "\n",
    "For the social progress score we only used the column which contained the Index itself. The remaining columns were dropped since they are essentially the different componenets of the social progress index. In future iterations and improvements of the project, other parameters can be added from this list e.g. Water and Sanitation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 344
    },
    "colab_type": "code",
    "id": "T1GQwqV9D-fg",
    "outputId": "c2bdd307-4baf-4a28-83d2-f3b2ec1d122b",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Code</th>\n",
       "      <th>Social Progress Index</th>\n",
       "      <th>Basic Human Needs</th>\n",
       "      <th>Foundations of Wellbeing</th>\n",
       "      <th>Opportunity</th>\n",
       "      <th>Nutrition and Basic Medical Care</th>\n",
       "      <th>Water and Sanitation</th>\n",
       "      <th>Shelter</th>\n",
       "      <th>Personal Safety</th>\n",
       "      <th>...</th>\n",
       "      <th>Drinking water violations - BUCKETED (0=high percent; 6=low percent)</th>\n",
       "      <th>Water fluoridation - CAPPED</th>\n",
       "      <th>Homelessness - CAPPED</th>\n",
       "      <th>Violent crime rate - CAPPED</th>\n",
       "      <th>Murder rate - CAPPED</th>\n",
       "      <th>Property crime - CAPPED</th>\n",
       "      <th>Carbon dioxide emissions - CAPPED</th>\n",
       "      <th>Quality of public transportation - CAPPED</th>\n",
       "      <th>Employment discrimination - CAPPED</th>\n",
       "      <th>Hate group concentration - CAPPED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>MA</td>\n",
       "      <td>64.82</td>\n",
       "      <td>67.94</td>\n",
       "      <td>62.33</td>\n",
       "      <td>64.32</td>\n",
       "      <td>77.74</td>\n",
       "      <td>65.32</td>\n",
       "      <td>57.01</td>\n",
       "      <td>73.60</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>70.4</td>\n",
       "      <td>0.257862</td>\n",
       "      <td>374.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1561.1</td>\n",
       "      <td>9.669469</td>\n",
       "      <td>4.38</td>\n",
       "      <td>7.384268</td>\n",
       "      <td>0.176165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>62.30</td>\n",
       "      <td>74.82</td>\n",
       "      <td>53.56</td>\n",
       "      <td>60.35</td>\n",
       "      <td>62.61</td>\n",
       "      <td>93.10</td>\n",
       "      <td>71.50</td>\n",
       "      <td>75.19</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>79.6</td>\n",
       "      <td>0.138914</td>\n",
       "      <td>240.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2133.3</td>\n",
       "      <td>15.996542</td>\n",
       "      <td>2.77</td>\n",
       "      <td>16.159561</td>\n",
       "      <td>0.181161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>VT</td>\n",
       "      <td>61.90</td>\n",
       "      <td>67.47</td>\n",
       "      <td>59.19</td>\n",
       "      <td>59.39</td>\n",
       "      <td>76.06</td>\n",
       "      <td>62.50</td>\n",
       "      <td>58.50</td>\n",
       "      <td>74.51</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>56.3</td>\n",
       "      <td>0.196127</td>\n",
       "      <td>156.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1697.4</td>\n",
       "      <td>9.743039</td>\n",
       "      <td>1.52</td>\n",
       "      <td>4.643016</td>\n",
       "      <td>0.160104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>NH</td>\n",
       "      <td>61.76</td>\n",
       "      <td>73.30</td>\n",
       "      <td>59.01</td>\n",
       "      <td>54.45</td>\n",
       "      <td>75.91</td>\n",
       "      <td>69.82</td>\n",
       "      <td>67.18</td>\n",
       "      <td>81.08</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>46.6</td>\n",
       "      <td>0.109080</td>\n",
       "      <td>196.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1512.9</td>\n",
       "      <td>11.352436</td>\n",
       "      <td>1.05</td>\n",
       "      <td>4.195401</td>\n",
       "      <td>0.449507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>CT</td>\n",
       "      <td>61.09</td>\n",
       "      <td>68.25</td>\n",
       "      <td>57.81</td>\n",
       "      <td>57.78</td>\n",
       "      <td>65.64</td>\n",
       "      <td>74.04</td>\n",
       "      <td>62.39</td>\n",
       "      <td>71.53</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>79.6</td>\n",
       "      <td>0.094731</td>\n",
       "      <td>224.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>10.182078</td>\n",
       "      <td>3.55</td>\n",
       "      <td>7.074050</td>\n",
       "      <td>0.139803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           State Code  Social Progress Index  Basic Human Needs  \\\n",
       "0  Massachusetts   MA                  64.82              67.94   \n",
       "1      Minnesota   MN                  62.30              74.82   \n",
       "2        Vermont   VT                  61.90              67.47   \n",
       "3  New Hampshire   NH                  61.76              73.30   \n",
       "4    Connecticut   CT                  61.09              68.25   \n",
       "\n",
       "   Foundations of Wellbeing  Opportunity  Nutrition and Basic Medical Care  \\\n",
       "0                     62.33        64.32                             77.74   \n",
       "1                     53.56        60.35                             62.61   \n",
       "2                     59.19        59.39                             76.06   \n",
       "3                     59.01        54.45                             75.91   \n",
       "4                     57.81        57.78                             65.64   \n",
       "\n",
       "   Water and Sanitation  Shelter  Personal Safety  ...  \\\n",
       "0                 65.32    57.01            73.60  ...   \n",
       "1                 93.10    71.50            75.19  ...   \n",
       "2                 62.50    58.50            74.51  ...   \n",
       "3                 69.82    67.18            81.08  ...   \n",
       "4                 74.04    62.39            71.53  ...   \n",
       "\n",
       "   Drinking water violations - BUCKETED (0=high percent; 6=low percent)  \\\n",
       "0                                                  3                      \n",
       "1                                                  6                      \n",
       "2                                                  3                      \n",
       "3                                                  5                      \n",
       "4                                                  4                      \n",
       "\n",
       "   Water fluoridation - CAPPED  Homelessness - CAPPED  \\\n",
       "0                         70.4               0.257862   \n",
       "1                         79.6               0.138914   \n",
       "2                         56.3               0.196127   \n",
       "3                         46.6               0.109080   \n",
       "4                         79.6               0.094731   \n",
       "\n",
       "   Violent crime rate - CAPPED  Murder rate - CAPPED  Property crime - CAPPED  \\\n",
       "0                        374.9                   2.0                   1561.1   \n",
       "1                        240.8                   1.8                   2133.3   \n",
       "2                        156.1                   2.2                   1697.4   \n",
       "3                        196.3                   1.3                   1512.9   \n",
       "4                        224.9                   2.2                   1808.0   \n",
       "\n",
       "   Carbon dioxide emissions - CAPPED  \\\n",
       "0                           9.669469   \n",
       "1                          15.996542   \n",
       "2                           9.743039   \n",
       "3                          11.352436   \n",
       "4                          10.182078   \n",
       "\n",
       "   Quality of public transportation - CAPPED  \\\n",
       "0                                       4.38   \n",
       "1                                       2.77   \n",
       "2                                       1.52   \n",
       "3                                       1.05   \n",
       "4                                       3.55   \n",
       "\n",
       "   Employment discrimination - CAPPED  Hate group concentration - CAPPED  \n",
       "0                            7.384268                           0.176165  \n",
       "1                           16.159561                           0.181161  \n",
       "2                            4.643016                           0.160104  \n",
       "3                            4.195401                           0.449507  \n",
       "4                            7.074050                           0.139803  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_scores_df = pd.read_excel('us-social-progress-index-results-public-4.xlsx')\n",
    "social_scores_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only State, Code, and Social Progress Index columns were used. Code will come in handy at a later stage, when plotting the choropleths. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nfMxuDAKD-fi",
    "outputId": "64f50ebc-0f4b-4f11-f318-25e46c17fa5a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Code</th>\n",
       "      <th>Social Progress Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>MA</td>\n",
       "      <td>64.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>62.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>VT</td>\n",
       "      <td>61.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>NH</td>\n",
       "      <td>61.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>CT</td>\n",
       "      <td>61.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           State Code  Social Progress Index\n",
       "0  Massachusetts   MA                  64.82\n",
       "1      Minnesota   MN                  62.30\n",
       "2        Vermont   VT                  61.90\n",
       "3  New Hampshire   NH                  61.76\n",
       "4    Connecticut   CT                  61.09"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_social_progress_code = social_scores_df.iloc[:, [0, 1, 2]]\n",
    "df_social_progress_code.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ViKuxaP1D-fj"
   },
   "outputs": [],
   "source": [
    "df = pd.merge(df, df_social_progress_code, on='State', how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below re-orders the columns for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GCSydQsdD-fl"
   },
   "outputs": [],
   "source": [
    "df = df[['State', 'Code', 'Happy State Index', 'GDP 2018', 'Life Expectancy', 'Employment', 'Social Progress Index']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fvqHueLCD-fm",
    "outputId": "d8e5cc78-dd77-4e9c-8beb-b2952c601faf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Code</th>\n",
       "      <th>Happy State Index</th>\n",
       "      <th>GDP 2018</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Social Progress Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>HI</td>\n",
       "      <td>68.27</td>\n",
       "      <td>51.28</td>\n",
       "      <td>81.3</td>\n",
       "      <td>97.6</td>\n",
       "      <td>49.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Utah</td>\n",
       "      <td>UT</td>\n",
       "      <td>67.84</td>\n",
       "      <td>44.64</td>\n",
       "      <td>80.2</td>\n",
       "      <td>96.9</td>\n",
       "      <td>50.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>67.26</td>\n",
       "      <td>53.70</td>\n",
       "      <td>81.1</td>\n",
       "      <td>97.1</td>\n",
       "      <td>62.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>65.62</td>\n",
       "      <td>62.84</td>\n",
       "      <td>79.5</td>\n",
       "      <td>97.4</td>\n",
       "      <td>53.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>63.14</td>\n",
       "      <td>53.99</td>\n",
       "      <td>80.8</td>\n",
       "      <td>95.8</td>\n",
       "      <td>45.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          State Code  Happy State Index  GDP 2018  Life Expectancy  \\\n",
       "0        Hawaii   HI              68.27     51.28             81.3   \n",
       "1          Utah   UT              67.84     44.64             80.2   \n",
       "2     Minnesota   MN              67.26     53.70             81.1   \n",
       "3  North Dakota   ND              65.62     62.84             79.5   \n",
       "4    California   CA              63.14     53.99             80.8   \n",
       "\n",
       "   Employment  Social Progress Index  \n",
       "0        97.6                  49.82  \n",
       "1        96.9                  50.58  \n",
       "2        97.1                  62.30  \n",
       "3        97.4                  53.14  \n",
       "4        95.8                  45.53  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d06785_2D-fo"
   },
   "source": [
    "### 5. GINI coefficient (statistical income distribution)\n",
    "\n",
    "Gini data was also web scrapped from wikipidea without issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KngvoZXRD-fo"
   },
   "outputs": [],
   "source": [
    "wiki = 'https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient'\n",
    "page = requests.get(wiki)\n",
    "soup = BeautifulSoup(page.content, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3gNOJ5xJD-fq"
   },
   "outputs": [],
   "source": [
    "title_page = soup.title.string\n",
    "assert 'Gini' in title_page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-rmiRZ5XD-ft"
   },
   "outputs": [],
   "source": [
    "right_table = soup.find(\"table\", class_='wikitable sortable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ld9hsyOuD-fv"
   },
   "outputs": [],
   "source": [
    "states, GINI = [], []\n",
    "\n",
    "for row in right_table.findAll('tr'):\n",
    "    \n",
    "    cells = row.findAll('td')\n",
    "    \n",
    "\n",
    "    if len(cells) == 0:\n",
    "        continue\n",
    "    \n",
    "    states.append(cells[1].find(text=True))\n",
    "    GINI.append(cells[2].find(text=True))\n",
    "\n",
    "assert len(states) == 52\n",
    "assert len(GINI) == 52"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data entries for the US overall and the District of Columbia had to be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TBLUTNzsD-fx",
    "outputId": "651b2210-3206-4cd7-b146-97b42190bee7"
   },
   "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>GINI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0.4063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>0.4081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>0.4304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>0.4360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>0.4420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  GINI\n",
       "State                 \n",
       "Utah           0.4063\n",
       "\n",
       "Alaska         0.4081\n",
       "\n",
       "New Hampshire  0.4304\n",
       "\n",
       "Wyoming        0.4360\n",
       "\n",
       "Hawaii         0.4420"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GINI_df = pd.DataFrame(list(zip(states, GINI)), \n",
    "            columns = ['State', 'GINI'])\n",
    "\n",
    "GINI_df.set_index('State', inplace=True)\n",
    "GINI_df = GINI_df.drop(['The United States Of America', 'District of Columbia'])\n",
    "\n",
    "assert GINI_df.size == 50\n",
    "GINI_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1 - GINI was used since GINI is between 0 and 1, with 0 being perfectly equal income and 1 being unequal. This way we can simply add the result without worrying about the sign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hwZ1KfLZD-f3",
    "outputId": "ec63cba8-2c18-43ba-e4f5-2516cb2a70a1",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Code</th>\n",
       "      <th>Happy State Index</th>\n",
       "      <th>GDP 2018</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Social Progress Index</th>\n",
       "      <th>GINI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>HI</td>\n",
       "      <td>68.27</td>\n",
       "      <td>51.28</td>\n",
       "      <td>81.3</td>\n",
       "      <td>97.6</td>\n",
       "      <td>49.82</td>\n",
       "      <td>0.5580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Utah</td>\n",
       "      <td>UT</td>\n",
       "      <td>67.84</td>\n",
       "      <td>44.64</td>\n",
       "      <td>80.2</td>\n",
       "      <td>96.9</td>\n",
       "      <td>50.58</td>\n",
       "      <td>0.5937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>67.26</td>\n",
       "      <td>53.70</td>\n",
       "      <td>81.1</td>\n",
       "      <td>97.1</td>\n",
       "      <td>62.30</td>\n",
       "      <td>0.5504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>65.62</td>\n",
       "      <td>62.84</td>\n",
       "      <td>79.5</td>\n",
       "      <td>97.4</td>\n",
       "      <td>53.14</td>\n",
       "      <td>0.5467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>63.14</td>\n",
       "      <td>53.99</td>\n",
       "      <td>80.8</td>\n",
       "      <td>95.8</td>\n",
       "      <td>45.53</td>\n",
       "      <td>0.5101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          State Code  Happy State Index  GDP 2018  Life Expectancy  \\\n",
       "0        Hawaii   HI              68.27     51.28             81.3   \n",
       "1          Utah   UT              67.84     44.64             80.2   \n",
       "2     Minnesota   MN              67.26     53.70             81.1   \n",
       "3  North Dakota   ND              65.62     62.84             79.5   \n",
       "4    California   CA              63.14     53.99             80.8   \n",
       "\n",
       "   Employment  Social Progress Index    GINI  \n",
       "0        97.6                  49.82  0.5580  \n",
       "1        96.9                  50.58  0.5937  \n",
       "2        97.1                  62.30  0.5504  \n",
       "3        97.4                  53.14  0.5467  \n",
       "4        95.8                  45.53  0.5101  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, GINI_df, on='State', how='inner')\n",
    "df['GINI'] = 1 - df['GINI'].astype(float)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fwH7Nd8GD-f6"
   },
   "source": [
    "End of Web Scraping and Data Collection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U9I14r_oD-f6"
   },
   "source": [
    "## Data Visualization and Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L_fXT4YTD-f6"
   },
   "source": [
    "### Histograms to study the near-normality of the distribution of variables\n",
    "\n",
    "Scipy's \"describe\" was used for a summary of main statistical measurements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Happy states Index is essentially unimodal and near-normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AHdAq7YLD-f6",
    "outputId": "82962560-62c7-4ac9-8afb-28e56b460634"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DpeeobWjl2mHR7g6lQ9QqFXdPSaDVauO9z6X8hHAdSQTC5+wuMhLgr2akh0wLXahXVDAzxg9gd1EFe0sq3R2O8BKSCIRPsdntFBw2kRLXE22An7vDuSLT0vrTu2cw724pprlVyk+IqyeJQPiUw6fOUW+2MCbB4LxzN6XxU/PzKYlU17Ww4R/H3R2O8AKSCIRP2VNsIsBfzYjBUe4O5arE9enBDaNi+byglONn6twdjvBwkgiEz7DbFb4triB5cE+0/p45LXShW9MH0yM4gLc+PYTVZnd3OMKDec61c0JcpcOl56gzW0hNdM+0kEqtotGVJaVVMHtSHG9uOshHu05yU2pfR5PWX4NGfuaJdpJEIHzGnuIKAjRqkge5Z1qoxWJjbycsNNMvOoSP807gp4ZQXQBw/i5mTTcvnSG6D/nNIHzC+WkhEyMGR3ns1UKXMnaoAbVaxdcHjHTjijGiG5NEIHzCkdPnqG1sddu0UGfSBfozKr4nZ6rMHCuXE8ei4yQRCJ+Qf8iEv0ZNsodfLXQp8X3D0YcHkn/IJPcWiA6TRCC8nl1RyD9cwYhBUQQGeOe8uUqlYlxSDBarjfxDsuC96BhJBMLrlZyupbahlTGJeuedPVh4qJbhg6I4Vl7HoZM17g5HeBBJBMLr5R+qQOPnmbWFOmrEoEjCdP6s/eIILZaOLYspfJckAuHV7IpCfnEFIwZFdvuVyFzBz09NWlIMlbXN/N9OKT8h2kcSgfBqx8rqONfQyhgvvFroUmKidKQlRbP5m1JOGb1zlS7hWpIIhFfbXWRE46cmJc77p4UuNCt9EMFBGt7OLfa65V6F60kiEF7LblfYU1xB8uAon5gWulBwoD93/HQIx8/U8eW3p90djujmJBEIr3Xk9PmVyMYO9Z1poQtdOyya4QMj+fAfx6iua3Z3OKIbk0QgvNY3RRUeuxKZK6hUKu7KSECxK7y75bCUnxCXJIlAeCWb3U5BcYVHr0TmCvrwIDKvH8j3JZV82wkF74R3kEQgvNKhk+dXIktN9KwF6jvD5NS+9DOE8O5nhzE3S/kJ8WOSCIRX+qbISGCAH8mDI90ditv5qdXcPTWRusZWPtx21N3hiG5IEoHwOlabnW+LTVwzRI+/xnenhS40sFcYPx3dh63flVFSVuvucEQ3065EsGnTJqZNm8bkyZNZtWrVj9qLiorIysoiIyODxYsXY7WeP/zMz88nKyuLmTNn8sADD1BbK3+AovMdOF6NucXqs1cLXcot1w8iPFTLO7nF2OyytKX4gdNEYDQaWblyJatXr2bjxo2sXbuWkpKSNn0WLlzI0qVL2bx5M4qisG7dOgAef/xxnnvuOTZt2kRcXBx//etfO2cUQlxgd5GR4EANSQNlWuhCQVoN824cwmlTA5/ny70F4gdOE0FeXh5paWmEh4ej0+nIyMggNzfX0V5WVkZzczMpKSkAZGVlOdo/+eQT4uLisFgsGI1GwsLCOmkYQpxnsdr47kglo+L1aPxk5vPfjYrXkzw4io3bj8u9BcLB6e2WFRUV6PU/lO81GAwUFhZesl2v12M0GgHw9/enuLiYe+65B41Gw6OPPtqh4KKiQjrU31Po9aHuDqHTuHtsu/aV09xq46a0Ae2KRak2ExoS6LRfe/o44++vccnrtIdOp0Ufqbto20NzruFXf/qKD7cf54nBere/Z53FW8cFrh+b00Rgt9tRqVSObUVR2mw7a09ISCAvL481a9awYMEC1qxZ0+7gqqoavK5Oil4fisnknYXAusPYPvv6JKE6f3qFa9sVi7nFSn3D5X8Zh4YEOu3THhaL8325itncgsl28TLUfsDM8f35cNsxdh88y0B9cJfE1JW6w99iZ7nc2NRq1RX9gHZ67BwTE4PJ9MONKCaTCYPBcMn2yspKDAYDLS0tfP75547Hb775ZoqLizscoBDt1dJqY+/RSsYkGPBTy7TQ5WSM7UfvnsG8vr5Q1i0QzhPB+PHj2bVrF9XV1TQ1NbFlyxbS09Md7bGxsWi1WgoKCgDIyckhPT0djUbDU089xf79+wH49NNPGTVqVCcNQwj49rCJVotdrhZqB42fmrsmx1NR08SmnSfcHY5wM6dTQ9HR0SxYsIDs7GwsFguzZ88mOTmZ+fPn8/DDDzNixAhWrFjBkiVLaGhoICkpiezsbPz8/Fi5ciVLly7FZrMRHR3NH/7wh64Yk/BRO/efQR8eyJC+4e4OxSMk9Ivgp6l92bz7FOOSoonVe+c5OeGcSunGlajkHIFncefYquuaWfhqHjdPGEjmhIHtfl5ji5U9RcbL9nHVOYKR8Xr2dlG9n9Sh0QS3o/R2QFAA9y/7nNiewTx256g25/c8ma9+zjrtHIEQniBv/1kUYPzwGHeH4lF6hGi5bVIch0/XsnPfWXeHI9zEt1brEF5JURR27jtDQt9w9OFB7g6nW1CpVTS2OC8wp1SbuSZBz7bvy1n75RHi+4UTHOTf4f1p/TVo5Gelx5JEIDze0fI6jDVNTBvX392hdBstFlu7pqH+Ne01fFAkH+XV8eZHB6/oqCp1aDQaH1sFzptIDhceb0dhOQH+asYkyNVCVyoiVMuwARGUnK6losbs7nBEF5NEIDyaudnK1weNjB0a7XPrErta8uCeBAdq+PqA0esu0hCXJ4lAeLRdB87SarEz6ZpYd4fi8fw1asYOi+ZcQysHT9a4OxzRhSQRCI+lKApbvytjQEwoA3tJQUNX6GsIoY8hhMKSShqaLO4OR3QRSQTCYx05XUtZZaMcDbjYv+7M3lNU4eZIRFeRRCA81lfflRGk1TB2qKxL7EohQf4kx/WktKKBU0bvvClLtCWJQHikmvoW8g9VcN3wGLQBshylqw3rH0F4SAC7iyqwWGU1M28niUB4pM/yS7ErCjem9nV3KF5JrVaRlhSNudlK4dFKd4cjOpkkAuFxzM0Wtn5XRmqiAYPcSdxpDBE64vr04OCJGmrqW9wdjuhEkgiEx9n6/flVyKZeK3cSd7ZR8XoCNH58fcBIN65PKa6SJALhUSxWG5/tKSVpQAT9Y7x3KcLuIjDAj9EJekznmigpq3V3OKKTyK2Yol2sdmixXL6ImVJtxtyOQmfOXK6A2Y7CM9Q2tvKLmcOuej+ifQbHhnG0rJaCYhN9DSEEBsjXhreRd1S0S4ul6+r2X6qAWXOrlZydJxjSpwfD+kdc9X5E+6hUKq5NimbTzhMUFJu4bkQvd4ckXEymhoTH+GxPKXWNrdx2Q5zXLKDiKcJDtCQNjORoWR3GailK520kEQiPUG9u5dNvTnHNkJ7E9enh7nB8UvLgKEKC/Pn6gBGbXe4t8CaSCIRH2JR3ghaLjVsnDnZ3KD5L46fm2mHR1Da2UlhS5e5whAtJIhDd3vEzdXxRcJqJI3vTu2ewu8PxabH6YAbHhrH/WDWVtU3uDke4iCQC0a1ZbXb+9skhwoIDmH2DHA10B6mJBoK0GnbuO4vNJlNE3kASgejWPv3mFKdNDWRPTkAX2PG1dIXrBfj7MW54NLUNreyVKSKvIIlAdFsnz9azaedxUhMNXBOvd3c44gKx+hDiYntw4Hg1ledkisjTSSIQ3VKduZWX1xcSqgvgzpvi3R2OuIgxiXqCAs9PEUmFUs8miUB0Ozabndc27KfObOGhW0cQFhzg7pDERQT4+zEuKYbaxlY+zjvh7nDEVWhXIti0aRPTpk1j8uTJrFq16kftRUVFZGVlkZGRweLFi7Faz5cZKCgoYPbs2WRmZnL33XdTVlbm2uiF17HbFVZ/dpji0nP8fEoiA2JkCcruLFYfzJA+Pfiy4DRFJ6rdHY64Qk4TgdFoZOXKlaxevZqNGzeydu1aSkpK2vRZuHAhS5cuZfPmzSiKwrp16xyPP/PMM+Tk5DBz5kyeeeaZzhmF8Ao2u8L2wjPsLqpg1vUDGTc8xt0hiXYYk2hAHxHEGx8dpN7c6u5wxBVwmgjy8vJIS0sjPDwcnU5HRkYGubm5jvaysjKam5tJSUkBICsri9zcXFpbW3nkkUdITEwEICEhgTNnznTSMISna2618tW3ZZw8W88t6YO4+bqB7g5JtJO/Rs3Ppw2l3mzhrU8PSblqD+S06FxFRQV6/Q9XbBgMBgoLCy/ZrtfrMRqNBAQEkJmZCYDdbufll1/mxhtvdGXswkucNjWQt+8srRYb45Ki+WlqXxpdUMW0PezyneUSfQ0hzL5hMGu/LGHrd2VMGtXH3SGJDnCaCOx2e5sCX4qitNl21t7a2sqiRYuwWq3cf//9HQouKiqkQ/09hV7veXX0lWozoSGBTvu1p8+/nK1qpOBQBSfO1BEZFkhm+mB6hgdhR0Vx6bmrCbfdEvpHuHxcl+Lvr3HJ67h6X66ISafTMm/qMI6U1bH2yxKuTY6lfy/3nt/xxM9Ze7l6bE4TQUxMDPn5+Y5tk8mEwWBo024ymRzblZWVjvbGxkZ++ctfEh4ezmuvvYa/f8duCKqqasDuZT/Z9PpQTKZ6d4fRYeYWq9MS087KUNvtCnXmVk6bGjl1tp7K2mYC/NWkDOlJ0oAI/PxU1Dc0Y7E435ertGdfriqv3d3GBa4bm9ncQpXNxl2T4/l/f61h2du7eTJ7DAH+flf92lfCUz9n7XG5sanVqiv6Ae00EYwfP56XXnqJ6upqgoKC2LJlC7///e8d7bGxsWi1WgoKChg9ejQ5OTmkp6cD508W9+/fn6eeegq1Wq5U9QV2RaGxyUK92UKduZX6xn/9u5WGJotjKiYyTMuYBD1D+objf6lVaITH6REcwL3Th/HC+3tZ82UJ2RkJ7g5JtIPTRBAdHc2CBQvIzs7GYrEwe/ZskpOTmT9/Pg8//DAjRoxgxYoVLFmyhIaGBpKSksjOzubgwYN88cUXxMXFccsttwDnzy+88cYbnT4o0TXsdgVTbROmc81UnWuizmyhtqEV+wUnCzV+KkJ1AYSHaukXHUpYcAAxUTpCgqRchLdKHhzFlGv7kfvNKeJiwxg/XBay6e7atULZzJkzmTlzZpvHLvxCT0xM5IMPPmjTPmzYMIqLi10QouhOFEWhoub8+rWnKxppsdgACAnyxxCho3fPYMJ0/oQGBxCmCyBI6yeLyPigWycO4nh5He/kFtPXEEpfg3ee7/MWslSlaBdFUTh5tp79x6qpqmvGX6Omjz6YvtGhREcEEaTVuGy+WXg+P7WaBzKT+N1be3hl/T6W/nyMFA3sxmRyVjhVWtHAi+8Xsu37cixWG2nDorlt0mCuH9mbATGhBF1kfWEheoRoeXDWcKrqmnnzo6I2U4aie5FPsLgku11hU94J/m/ncXRaDWlJ0cT16YFapnpEOw3pE87tP4njvc+P8OnXJ5k+boC7QxIXIYlAXFRtQwv/s+kgRSdrSEuKZlb6IA4el1oyouNuHN2HY+V1rP/HMfrHhDJ8YJS7QxL/RqaGxI+UVTby9Nv5HC2r5Z6picyfMYxgmd8VV0ilUnH3lARiewbzl40HMFab3R2S+DeSCEQbR06fY/m7BdjtCo//bDTXj+wtV/2IqxYYoOGhW5NRq1W8+GEh5uauKSEi2kcSgXAoOlnDijXfExLkzxN3jaZ/jPfeoi+6nj48iF/dMpyKmib+8n/7va5qgCeTRCAAKDldy4sfFGIID+Lxu0ajDw9yd0jCCyX0i+DOm+LZf6yaD7YedXc44p/kZLEHs9qhxXL1h9ilxnpe/KCQsOAAHswagZ+f+kfVP+XHm7gclVrV7oqxqcOiOX62ntzdp+gZHsi1SR1bd0Lrr0GqkriWJAIP1mKxsqfIeFWv0dBk4dOvT+KnVpE+shfFp2ou2m+kLB4vLqPFYmPvYZPzjv/UPzqEI5E6Vn92hKraZvQR7T8CTR0ajUbuXXEpyas+rNVq48uC01htCj8d3Ydgqf8juoharSI9pTfBQRq++q6MxmaLu0PyaZIIfJRdUdi+9wy1ja1MTOlNeKjW3SEJHxMY4MekUbHYbApffVuG1WZ3d0g+SxKBjyosqaLM1MjYoQZ69wx2dzjCR4WHaLl+ZC+q61rI23dWlrl0E0kEPqi0ooHCo1XExfYgvm+4u8MRPq6PIYRR8T058c+ihqLryRkXH1NvbmVH4Rkiw7SMHWaQm8VEt5A0MJK5kkJIAAAZEElEQVSa+ha+O1JJj5AA+kXLPSxdSY4IfIjNbucf35ejUsENKbFo/OTtF92DSqVi3PAYosIC2VF4hpr6FneH5FPkm8CHfFtcSVVdC+OHxxCikyuERPei8VMzaVRv/DVqvvq2jOZWm7tD8hmSCHzE6YoGik7WkNgvXA67RbelC/TnhmtiMbdY+cf35VKGootIIvABTS1Wdu47S0SoltGJcmOY6N704UGMS4rmbLWZPYcq3B2OT5CTxV5OURTy9p/FarNz/che+Kkl94vub3BsD2rqWzh4ooaIUK1c3dbJ5FvByx0praXM1MioBD3hIXLTmPAcoxL09O4ZzDcHjbKGQSeTRODF6hpbyS+uoFeUjsR+8otKeBa16nz9q9Agf7Z9Xy5lKDqRJAIvZbcr7Cg8g1qt4roRMXK/gPBIAf5+3DAqFqvNzrbvyrHZpQxFZ5BE4KUKj1ZRWdtMWlIMOllmUniw8BAt143oRWVtM3uK5ORxZ5BE4IVM55rYd6yKQb3DGCCrjAkv0D8mlKSBkRwureXrA2fdHY7XaVci2LRpE9OmTWPy5MmsWrXqR+1FRUVkZWWRkZHB4sWLsVrbLlDxwgsv8NJLL7kmYnFZFqudHYVn0Gk1jB1qcHc4QrjMNUN6EhOpY+0XRzhxts7d4XgVp4nAaDSycuVKVq9ezcaNG1m7di0lJSVt+ixcuJClS5eyefNmFEVh3bp1ANTX1/PEE0/wt7/9rXOiFz9SUFxBvdnCdcm9CPD3c3c4QrjM+TUMehGqC+CV9ftpaJKTx67iNBHk5eWRlpZGeHg4Op2OjIwMcnNzHe1lZWU0NzeTkpICQFZWlqP9iy++YMCAAdxzzz2dFL640OmKBg6X1pI0MIKYSJ27wxHC5QIDNPzHjGHUNrbwes5+ufPYRZwmgoqKCvT6H+5GNRgMGI3GS7br9XpH+6xZs7jvvvvw85Nfpp2tqcVK3v7zdw+nDOnp7nCE6DT9Y0L52eQEDpyoYcP2Y+4Oxys4vbPYbre3ufRQUZQ2287ar0ZUVIhLXqe70etdcwJXqTYTGhKIoihs+74ci9XOrIn9CQ9r//qv7eXvryE0JNBpv/b0cdW+XMHXxwWeNzadTsutNyZwpqaJj3edZGRCNONG9PpRP1d9zrojV4/NaSKIiYkhPz/fsW0ymTAYDG3aTaYfFq2urKxs0341qqoavO7QT68PxWSqd8lrmVus1Dc0c+hkDSfP1jN2qIEAPxX1Dc0uef0LWSxWp68bGhLokn23Z1+u4svjAs8cm9ncgslmI2vCQA6frOb51QU8efcYekX9sNKeKz9n3c3lxqZWq67oB7TTqaHx48eza9cuqquraWpqYsuWLaSnpzvaY2Nj0Wq1FBQUAJCTk9OmXXSumvoW8otNxOqDSZC7h4UP8deo+dUtI/DXqHl5/T6aWqzOnyQuymkiiI6OZsGCBWRnZzNr1ixmzJhBcnIy8+fPZ9++fQCsWLGCZcuWMWXKFMxmM9nZ2Z0euDh/qej2veUEaNSMHy53DwvfExkWyAOZwzlbbeZ/PymSNY+vULuqj86cOZOZM2e2eeyNN95w/HdiYiIffPDBJZ//0EMPXWF44nL+b8dxzjW08tPRfQjSSiFZ4ZuG9o/gthviWPdVCbnfnGJqWn93h+Rx5M5iD7XvWBVbvytjaP8IYvXBzp8ghBfLGNuX1EQDH2w7yoET1e4Ox+NIIvBAVbXNvLHpIL17BjMqXi4VFUKlUnHPtER6RwXzes4BKVvdQZIIPIzFaufVjfuw2e38x4yh+MkC9EIA5282+8+sEdjsdpa9vZtWi6x53F7yLeJh3vv8MMfP1PMf04dhiJC7h4W4UHSkjvkzkjh6upa/by6Wk8ftJInAg+woPMPW78uZltafUfGy9rAQF5MypCdzb0pg5/6zbP2uzN3heARJBB7ilLGev28pZmj/CG5JH+jucITo1u6YnEDy4ChWf36E4lM17g6n25NE4AEamy28vH4fIUH+3H9zkixAL4QTarWK+2YOwxARxMvr93FWTh5flnyjdHNWm51XN+ynpr6FB28ZTlhwgLtDEsIj6AL9eeS2kahUKl54f6+Urb4MSQTdmKIovLO5mKKTNfx8aiKDe/dwd0hCeBRDeBAP35pMdV0LL39YiMUqax5fjCSCbuzjXSfZUXiGmeMHcN1FqisKIZyL69ODe6cncvh0LW99KmUoLkbqEnRT/9hbzvp/HCNtWDSzrpeTw0JcjbRhMVTUNLFx+3GiI3TcPEE+UxeSRNAN7S4y8vanhxgxKIp7pw+VYnJCuMDM8QPOJ4MdxwkNDmDSNbHuDqnbkETQzXx/pJI3Nh0krk8PHrxlOBq5c1gIl1CpVPx8aiINTRbe3VxMkNaPtGEx7g6rW5BvmW6koLiCVzbso190CI/MTkYri88L4VIaPzUPzhrOkL7h/PWjIr47bHL+JB8giaCb+Oagkdc2HmBAr1D+a8416AL93R2SEF4pwN+PR2Yn0z8mlFc37qeguMLdIbmdJAI3UxSFzbtP8fr/HSAuNoxHb09BFygzdkJ0piCthv+ak8LAXmG8tvEAu4uM7g7JrSQRuJHNbue9z4+w9ssSxiTo+a+5KbLAjBBdJEirYcHtI4mLDeP1nAN8nl/q7pDcRhKBm9SZW3l+7V4+LzjN5NS+PDBrOP4aOScgRFcK0mp4dE4KKUN6svrzI7z/VQl2H7zPQH5+usHRslpey9lPXaOFe6cNZUKy3CwmhLsE+Pvxq1tGsOqzw3z6zSnKKxuZPzPJp6Zo5YigC1ltdt7NLeKP7xagVql44q5RkgSE6AbUahU/mxzPnTfFs/94Nb9/J5/TpgZ3h9VlfCfludmx8jr+vrmYk8Z6rhsew7yb4uV8gBDdiEql4qej+9DXEMKrG/fz9Fv5zJ44iBtT+6L28ps65Zuok9U1trJx+zG2fV9OWEgAj9+dypBeoe4OSwhxCfF9w3n63rG89ekh1nxZwvclldyVkUCvqGB3h9ZpJBF0EnOzlc27T7FlTykWq52bUvuSOWEg/fpEYDLVuzs8IcRlhAUH8NCtI9heeIZ1X5aw9K+7mTy2LzPGDfDKI3nvG5GbVdU281l+Kf/YW05zq40xiQZuuX6gV/+aEMIbqVQq0kf2JiWuJ+9vLeHTr0/xj+/LmXJtP346ug+BAd7z9ek9I3Eji9XG3pIqtheeYf/xKlSoSB1qYMrYfvSPkWkgITxZWHAA/zF9GD8d3YeN24/z4bZjfPL1KSaM6MVPRscSHaFzd4hXTRLBFaptaOHgyRq+P1JJ4dEqWiw2IkK1TEvrzw0psUT1CHR3iEIIFxoQE8avbxvJsfI6tuw5xZffnuaz/FIGx4aRNiyGa4b0JDLMMz/37UoEmzZt4rXXXsNqtXL33Xdz5513tmkvKipi8eLFNDY2MmbMGJ566ik0Gg3l5eUsXLiQqqoqBg4cyIoVKwgO9rwpkuZWK8bqJk4a6zlWXsfRslrKKhsBCNP5k5YUzegEPcP6R6JWe/fVBUL4ukG9w3ggczg19S3k7T/DNweNrPrsMKs+O0xsz2CGDoggLrYHg3v3IDJM6xFl5J0mAqPRyMqVK1m/fj0BAQHMnTuXa6+9lri4OEefhQsX8swzz5CSksITTzzBunXrmDdvHk899RTz5s1j+vTpvPLKK7z66qssXLiwUwfUEYqiYLXZMbfYqG9s5VxjC7UNrdQ1tlJd18LZ6kbOVJuprmtxPEen1TCwdxjjhscwbEAE/Qyh8uUvhA+KCNUyfdwApo8bQFllI/uOVrHvWBXbvi/n8/zTAAQG+BEdqaNXpI6YSB2GiCDCggMI1QUQpvMnROePn9r9t3M5TQR5eXmkpaURHh4OQEZGBrm5ufznf/4nAGVlZTQ3N5OSkgJAVlYWL774Irfddht79uzhlVdecTz+s5/9rEOJ4Eq+YFstdt7fWkK9uRWbAtgVbHYFu3L+H0WB1lYbLZbz/9jsF7+dXOvvhyEiiLFDozGEB2GICMIQqaNnj8CrvqbYVYlD46fusiql7dlXkFaDzXr18ci4rl579+WpY2vPZ6grf6D1NYTQ1xDCtHH9sdntlFeaKa1owHSu6fw/NU0cP1PHxb5tAgP88Nf44a9REeDvh7+fH5NGxTJ8YOQl93epsV3pmJ0mgoqKCvR6vWPbYDBQWFh4yXa9Xo/RaKSmpoaQkBA0Gk2bxzsiIuLKppEenjvqip7XVaKiQlz2Wn16dd2C9oP6RMi+ZF/dZn/OuPJz1lEGfRgpQzvv9V09NqfHJHa7vc0cl6IobbYv1f7v/QCPmCsTQghf4zQRxMTEYDL9sIqPyWTCYDBcsr2yshKDwUBkZCT19fXYbLaLPk8IIUT34DQRjB8/nl27dlFdXU1TUxNbtmwhPT3d0R4bG4tWq6WgoACAnJwc0tPT8ff3Z8yYMXzyyScAbNy4sc3zhBBCdA8qRXFefHvTpk28/vrrWCwWZs+ezfz585k/fz4PP/wwI0aM4NChQyxZsoSGhgaSkpJYtmwZAQEBlJWVsWjRIqqqqujVqxfPP/88PXp03Zy2EEII59qVCIQQQngv91/AKoQQwq0kEQghhI+TRCCEED5OEoEQQvg4qT7aif785z+zefNmVCoVs2fP5p577iEvL49ly5bR0tLC1KlTWbBggbvDvCrPPvssNTU1LF++/JLFBz3JXXfdRXV1tSPup59+mlOnTl226KKn+PLLL3n55ZdpamriuuuuY8mSJV7x9/j+++/z7rvvOrZPnz5NZmYmN954o8ePLScnh//5n/8BID09nccee6xzPmeK6BTffPONMnfuXMVisShNTU3KpEmTlKKiImXixInKqVOnFIvFotx7773K1q1b3R3qFcvLy1OuvfZa5bHHHlMURVGmT5+ufPfdd4qiKMrjjz+urFq1yp3hdZjdblcmTJigWCwWx2Nnz55VJk2apNTU1CiNjY3KzJkzlSNHjrgxyitz6tQpZcKECcqZM2eU1tZW5Y477lC2bt3qVX+PiqIohw8fVm666SalvLzc48dmNpuV1NRUpaqqSrFYLMrs2bOVnTt3dsrnTKaGOsnYsWN555130Gg0VFVVYbPZqKuro3///vTt2xeNRsPMmTPJzc11d6hX5Ny5c6xcuZIHHngAuHjxQU8b27FjxwC49957ufnmm3n33XfbFF3U6XSOooue5rPPPmPatGnExMTg7+/PypUrCQoK8pq/x3/53e9+x4IFCygtLfX4sdlsNux2O01NTVitVqxWKxqNplM+Z5IIOpG/vz8vvvgi06dPZ9y4cRct4NfRQnzdxdKlS1mwYAFhYWHApYsPepK6ujrGjRvHK6+8wltvvcWaNWsoLy/3ivfs5MmT2Gw2HnjgATIzM1m9erVX/T3C+UrJzc3NTJ061SvGFhISwiOPPMLUqVOZOHEisbGx+Pv7d8rnTBJBJ3v44YfZtWsXZ86c4cSJE5ct4Ocp3n//fXr16sW4ceMcjzkrTugJrrnmGp577jlCQ0OJjIxk9uzZvPjiix4/Ljj/63LXrl388Y9/ZO3atRQWFlJaWuoVY/uXNWvWcM899wDe8fd46NAhPvzwQ7766iu2b9+OWq1m586dnTIuzzqT50GOHj1Ka2srQ4cOJSgoiMmTJ5Obm4ufn5+jj6cW4vvkk08wmUxkZmZSW1uL2WxGpVJdtPigJ8nPz8disTgSnKIoxMbGXrbooqfo2bMn48aNIzLyfI37G2+80Wv+HgFaW1vZs2cPy5cvB5wXy/QEO3bsYNy4cURFRQHnp4H++te/dsrnTI4IOsnp06dZsmQJra2ttLa28sUXXzB37lyOHz/uOEz/6KOPPLIQ39/+9jc++ugjcnJyePjhh/nJT37CsmXLLlp80JPU19fz3HPP0dLSQkNDAxs2bOBPf/rTZYsueopJkyaxY8cO6urqsNlsbN++nSlTpnjF3yNAcXExAwYMQKc7v5D8yJEjPX5siYmJ5OXlYTabURSFL7/8krFjx3bK50yOCDrJxIkTKSwsZNasWfj5+TF58mSmT59OZGQkDz30EC0tLUycOJEpU6a4O1SXWbFiRZvig9nZ2e4OqUMmTZrE3r17mTVrFna7nXnz5jF69GgWLFhAdna2o+hicnKyu0PtsJEjR/KLX/yCefPmYbFYuO6667jjjjsYNGiQV/w9lpaWEhMT49jWarUsX77co8c2YcIEDh48SFZWFv7+/owYMYL77ruPm266yeWfMyk6J4QQPk6mhoQQwsdJIhBCCB8niUAIIXycJAIhhPBxkgiEEMLHSSIQXS4hIYHq6uo2j61fv57777/fTRGdt3XrVubMmcPNN9/M9OnTeeSRRzh79ixw/h6D9lym195+/+6ll17i6aef7tBzqqurSUhI6PC+hPh3ch+BEIDRaOSxxx5j/fr1xMbGAvDaa6/x61//mjVr1lBbW8u+ffucvk57+wnRnUgiEN3O8ePHefrpp2lsbMRkMpGYmMgLL7yAVqtl2LBhzJ8/n+3bt2M2m3n00UeZPHky69evJzc3F7vdTnl5OdHR0SxfvhybzcaMGTPYtm0boaGhKIrClClT+POf/0xiYqJjnzU1NVgsFsxms+Oxu+++29Hn8ccfp7m5mczMTNavX8+GDRtYu3YtFouF2tpa5s+fz7x5837U78SJE/zhD3/g3Llz2Gw27rrrLmbPnn3Z8S9atIiQkBCKi4s5e/YsCQkJPPvsswQHB7NlyxZH5dDhw4e3ed7777/Pe++9h91uJzw8nCeffJKBAwdyzz33kJSUxG9/+1vy8vJYtGgR69evp2fPni5814RHu+pC1kJ0UHx8vDJjxgzl5ptvdvwzceJE5b777lMURVGWL1+ubNy4UVEURWltbVVmzJih5ObmOp772muvKYqiKEVFRcro0aOVqqoq5cMPP1RSUlKUY8eOKYqiKH/605+Uhx56SFEURfnlL3+pvPvuu4qinF9D4fbbb79oXMuWLVOSkpKUqVOnKosXL1Y++ugjx9oEpaWlSkpKiqIoitLQ0KDcfvvtSnV1taIoivLdd9852i7sZ7FYlGnTpin79+9XFEVR6urqlKlTpzpqyV/oxRdfVJ566ilFURTlscceU+bMmaO0tLQora2tyqxZs5QPPvhAMZlMyujRox3rIfzlL39R4uPjFUU5v/7FvHnzFLPZrCiKomzfvl2ZMmWKoiiKYjQalfHjxyufffaZcv311yu7d+9u/5slfIIcEQi3ePvttx0F0OD8OYLNmzcDsHDhQnbu3Mkbb7zBiRMnqKioaPNL/Wc/+xlwvhZLfHw8e/bsAeC6665j4MCBANx+++1kZmYCcOedd/KnP/2JO++8k7Vr13LHHXdcNKZFixZx//33s3v3bvbs2cNzzz3H3//+d1atWtWmX3BwMH/5y1/Ytm0bJ06c4NChQ23i+5cTJ05w6tQpnnjiCcdjzc3NHDx40FFP/lKuv/56AgICAIiPj6e2tpaCggLi4+OJi4sDYM6cOTz//PPA+fMbJ0+eZO7cuY7XqKur49y5cxgMBn7/+9/z4IMP8tBDD5GamnrZfQvfI4lAdDuPPvooNpuNqVOncsMNN3DmzBmUCyqhXFgx0263O7Yv9fj48eNpampi165d5Ofn8+yzz/5on1988QXnzp3j1ltvJSMjg4yMDBYsWMDEiRM5ePAgERERjr5nz55lzpw53H777YwePZopU6bw1Vdf/eg1bTYboaGh5OTkOB6rrKwkNDTU6f+DwMBAx3+rVCrH+C/8/3Dh8oR2u53MzEwWLlzo2K6oqKBHjx4AlJSU0LNnTwoLC53uW/geuWpIdDs7duzgV7/6FdOmTQNg79692Gw2R/vGjRsBOHDgAMePH3f8wv36668di3SsWbOGSZMmAee/SOfNm8fixYuZMWMGWq32R/sMDg7m+eefp6SkxPFYaWkpfn5+9OvXD41Gg81mQ1EU9u/fT2RkJA8++CATJkxwJAGbzdam38CBAwkMDHQkgjNnzjBjxgz2799/Rf9fUlNTKSkp4dChQ8D5o6h/mTBhAh9//DEVFRUAvPfee9x9990AFBYW8s477/Dhhx9SX1/P22+/fUX7F95LjghEt7NgwQJ+9atfodPpCAkJITU1lVOnTjnav/32W9atW4fdbmflypWOX73R0dEsXLgQk8lEXFxcm8sxb7nlFp599lnmzJlz0X2mpaXx5JNP8thjj1FfX4+fnx96vZ433niDHj16EBISQnJyMtOnT+dvf/sb0dHRTJkyBZVKxdixY4mMjOTkyZP079/f0W/VqlW8+uqr/OEPf+DNN9/EarXyyCOPMHr06Cv6/xIZGcmKFSv4zW9+g7+/f5spngkTJjB//nzuvfdeVCoVISEhvPzyyzQ2NvLoo4+yZMkSxwn02267jdTUVIYNG3ZFcQjvI9VHhUdJSEhg165dbc4vwA/nGF5//fWLPu/jjz9mw4YNvPnmm10RphAeRY4IhNe76667qK6u5tVXX3V3KEJ0S3JEIIQQPk5OFgshhI+TRCCEED5OEoEQQvg4SQRCCOHjJBEIIYSPk0QghBA+7v8DPAXZopPkhaAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=50, minmax=(33.42, 68.27), mean=52.0518, variance=73.68615383673468, skewness=-0.1507820964197043, kurtosis=-0.4976419174116433)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.distplot(df['Happy State Index'])\n",
    "plt.show()\n",
    "stats.describe(df['Happy State Index'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GDP is also near-normal, with "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PWHyIpI1D-f8",
    "outputId": "88831479-72da-4f78-a012-ce0de66c6fb5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=50, minmax=(31.88, 65.55), mean=48.306799999999996, variance=75.32091608163265, skewness=0.3212730591057801, kurtosis=-0.6737867753447708)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['GDP 2018'])\n",
    "stats.describe(df['GDP 2018'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hJWxsbYlD-f-",
    "outputId": "2cd7c478-a8e7-4d1e-9792-7a1cb94df236"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=50, minmax=(75.0, 81.3), mean=78.662, variance=2.7432204081632623, skewness=-0.5908895235688668, kurtosis=-0.5468653514039543)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Life Expectancy'])\n",
    "stats.describe(df['Life Expectancy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y6pxn5HFD-f_",
    "outputId": "438db62b-186e-489d-afc9-2e30b60db19e",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=50, minmax=(93.4, 97.6), mean=96.22400000000002, variance=0.6720653061224473, skewness=-0.7023148778828004, kurtosis=1.2463591989385812)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Employment'])\n",
    "stats.describe(df['Employment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Aj4RCASjD-gA",
    "outputId": "d6f9b59a-e415-4fb3-ee9b-272ee62372b2",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=50, minmax=(27.18, 64.82), mean=48.2122, variance=84.84994404081631, skewness=-0.43789314568478344, kurtosis=-0.41574833378638676)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Social Progress Index'])\n",
    "stats.describe(df['Social Progress Index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "b8eR5PuxD-gC",
    "outputId": "af97bafe-f710-4a2a-a17d-800dc7997759"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=50, minmax=(0.47709999999999997, 0.5937), mean=0.5353519999999999, variance=0.00044164417959183705, skewness=0.34169543910818007, kurtosis=1.2504327547735983)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['GINI'])\n",
    "stats.describe(df['GINI'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TUknQuGsD-gE"
   },
   "source": [
    "### Barplot for the Happy State Index\n",
    "\n",
    "This shows the gradual near-linear decrease oh happiness in the US (as per the Happy State Index)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZUP2vALqD-gE",
    "outputId": "877e1bb4-72dc-4711-e214-0d074a9f00bf",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "plt.bar(x=df['State'], height=df['Happy State Index'], width=0.8)\n",
    "plt.title('Happy State Index')\n",
    "plt.xticks(df['State'], rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "41Dp6hWoD-gI"
   },
   "source": [
    "### Choropleth plots to capture the geospatial relationships between variables\n",
    "\n",
    "Choropleth maps were appropriate this the data is predominantely geospatial. The colors were used to represent high intensity in red and low in blue, as for a basic heatmap. Plotly was chosen due to their friendly UI and ability to interact with the plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4pB28Y7sD-gI"
   },
   "source": [
    "Choropleth map tutorial from https://plot.ly/python/choropleth-maps/?utm_source=mailchimp-jan-2015&utm_medium=email&utm_campaign=generalemail-jan2015&utm_term=chloropleth-maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "okvw3V7sD-gI",
    "outputId": "7586f304-af88-4b95-9561-af96b01a4a08"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "colorbar": {
          "title": {
           "text": "0-100"
          }
         },
         "colorscale": "RdBu",
         "locationmode": "USA-states",
         "locations": [
          "HI",
          "UT",
          "MN",
          "ND",
          "CA",
          "ID",
          "MD",
          "IA",
          "SD",
          "NE",
          "WI",
          "CT",
          "NJ",
          "NY",
          "VA",
          "MA",
          "WA",
          "CO",
          "GA",
          "NC",
          "AZ",
          "TX",
          "IL",
          "NH",
          "KS",
          "NV",
          "DE",
          "MT",
          "FL",
          "PA",
          "RI",
          "IN",
          "ME",
          "MI",
          "WY",
          "SC",
          "OH",
          "VT",
          "OR",
          "TN",
          "NM",
          "MO",
          "MS",
          "KY",
          "AL",
          "OK",
          "AK",
          "LA",
          "AR",
          "WV"
         ],
         "marker": {
          "line": {
           "color": "rgb(255,255,255)",
           "width": 2
          }
         },
         "type": "choropleth",
         "uid": "6ab25945-80a6-4d52-a774-b855f7a6b218",
         "z": [
          68.27,
          67.84,
          67.26,
          65.62,
          63.14,
          63.09,
          61.78,
          61.07,
          60.8,
          59.11,
          58.87,
          58.24,
          58.1,
          57.35,
          57.35,
          56.85,
          56.44,
          55.38,
          55.15,
          53.69,
          52.92,
          52.56,
          52.47,
          52.37,
          52.3,
          51.8,
          51.67,
          51.08,
          51.01,
          50.54,
          50.44,
          50.2,
          49.81,
          49.52,
          48.9,
          48.26,
          48.19,
          48.18,
          48.05,
          46.13,
          43.35,
          42.76,
          41.63,
          39.42,
          39.35,
          38.89,
          38.21,
          37.15,
          36.61,
          33.42
         ]
        }
       ],
       "layout": {
        "geo": {
         "projection": {
          "type": "albers usa"
         },
         "scope": "usa",
         "showlakes": false
        },
        "title": {
         "text": "Happy State Index by State<br>(Hover for breakdown)"
        }
       }
      },
      "text/html": [
       "<div>\n",
       "        \n",
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    "data = [go.Choropleth(\n",
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    "## Linear Regression to determine coefficients for each variable\n",
    "\n",
    "Simple linear regression models were used in order to determine the r^2 value in each category. A summative interpretation will be offered in a later section."
   ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['GDP 2018'], df['Happy State Index'])\n",
    "plt.title('GDP 2018 vs Happy State Index')\n",
    "plt.ylabel('Happy State Index (0-100)')\n",
    "plt.xlabel('GDP per capita in $1000 (2018)')\n",
    "\n",
    "X = np.array(df['GDP 2018']).reshape((-1, 1))\n",
    "y = df['Happy State Index']\n",
    "\n",
    "model = LinearRegression().fit(X, y)\n",
    "r_sq_gdp = model.score(X, y)\n",
    "print(round(r_sq_gdp, 3))\n",
    "\n",
    "y_pred = model.predict(X)\n",
    "plt.plot(X, y_pred, color='red', linewidth=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hQCyBmD3D-gV",
    "outputId": "10d899a2-7d1d-4f8a-c333-9c2a9a74753c",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.647\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Life Expectancy'], df['Happy State Index'])\n",
    "plt.title('Life Expectancy vs Happy State Index')\n",
    "plt.ylabel('Happy State Index (0-100)')\n",
    "plt.xlabel('Life Expectancy (years)')\n",
    "\n",
    "\n",
    "X = np.array(df['Life Expectancy']).reshape((-1, 1))\n",
    "y = df['Happy State Index']\n",
    "\n",
    "model = LinearRegression().fit(X, y)\n",
    "\n",
    "r_sq_le = model.score(X, y)\n",
    "print(round(r_sq_le, 3))\n",
    "\n",
    "y_pred = model.predict(X)\n",
    "plt.plot(X, y_pred, color='red', linewidth=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nbgMdZqPD-gX",
    "outputId": "5a1c79d5-508f-4188-f6cd-8f8583ea83bc",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.323\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Employment'], df['Happy State Index'])\n",
    "plt.title('Employment vs Happy State Index')\n",
    "plt.ylabel('Happy State Index (0-100)')\n",
    "plt.xlabel('Employment Rate (%)')\n",
    "\n",
    "X = np.array(df['Employment']).reshape((-1, 1))\n",
    "y = df['Happy State Index']\n",
    "\n",
    "model = LinearRegression().fit(X, y)\n",
    "r_sq_emp = model.score(X, y)\n",
    "print(round(r_sq_emp, 3))\n",
    "\n",
    "y_pred = model.predict(X)\n",
    "plt.plot(X, y_pred, color='red', linewidth=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y9wbJvBWD-ga",
    "outputId": "2a56a9bc-10d5-4819-ffae-048e69ea5beb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.468\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Social Progress Index'], df['Happy State Index'])\n",
    "plt.title('Social Progress Index vs Happy State Index')\n",
    "plt.ylabel('Happy State Index (0-100)')\n",
    "plt.xlabel('Social Progress Index (0-100)')\n",
    "\n",
    "X = np.array(df['Social Progress Index']).reshape((-1, 1))\n",
    "y = df['Happy State Index']\n",
    "\n",
    "model = LinearRegression().fit(X, y)\n",
    "r_sq_soc = model.score(X, y)\n",
    "print(round(r_sq_soc, 3))\n",
    "\n",
    "y_pred = model.predict(X)\n",
    "plt.plot(X, y_pred, color='red', linewidth=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FuORPXfoD-gc",
    "outputId": "820c36ad-8d76-44e5-c95e-84a613d960e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.062\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['GINI'], df['Happy State Index'])\n",
    "plt.title('Gini Coefficient vs Happy State Index')\n",
    "plt.ylabel('Happy State Index (0-100)')\n",
    "plt.xlabel('Gini Coefficient (0-1)')\n",
    "\n",
    "X = np.array(df['GINI']).reshape((-1, 1))\n",
    "y = df['Happy State Index']\n",
    "\n",
    "model = LinearRegression().fit(X, y)\n",
    "r_sq_gini = model.score(X, y)\n",
    "print(round(r_sq_gini, 3))\n",
    "\n",
    "y_pred = model.predict(X)\n",
    "plt.plot(X, y_pred, color='red', linewidth=1.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zmMkrsswD-gd"
   },
   "outputs": [],
   "source": [
    "coef = [r_sq_gdp, r_sq_le, r_sq_emp, r_sq_soc, r_sq_gini]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most strongly correlated category was Life Expectancy, which makes sense intuitively, since it is often considered conventional wisdom that a happy life is a long life."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vJCEM-VuD-ge",
    "outputId": "6b563747-3acd-468a-bb31-7385fec07523"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.19592026304861865,\n",
       " 0.6467985519986392,\n",
       " 0.3234383621873219,\n",
       " 0.46798557295804843,\n",
       " 0.06167995494622214]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coefficients were normalized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yjjgt1wxD-gf"
   },
   "outputs": [],
   "source": [
    "norm = [x / sum(coef) for x in coef]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1FBWlpqFD-gg",
    "outputId": "108865c6-07e1-4ac7-d505-1c0f7550aec5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.11553110030601761,\n",
       " 0.38140694191594793,\n",
       " 0.19072651946881408,\n",
       " 0.2759637381548862,\n",
       " 0.036371700154334184]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A vertical barplot was used to look at the r^2 values of each parameter. Life expectancy was the most correlated, followed by social progress, then employment, GDP and GINI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_LfCLdn4D-gk",
    "outputId": "8d72c876-c1f1-4952-a323-2bd7ac4d6e32"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels =['GDP 2018', 'Life Expectancy', 'Employment', 'Social Progress', 'Gini']\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(x=labels, height=norm, width=0.8)\n",
    "plt.title('Weights of each Category')\n",
    "plt.xticks(labels, labels, rotation=30)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we see a correlation matrix, we can see that life expectancy correlates the most with HSI (the reasons were explained above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GaZqmHb2D-gm",
    "outputId": "43e0bfb4-55fc-439e-ace1-96d32079f607"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happy State Index</th>\n",
       "      <th>GDP 2018</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Social Progress Index</th>\n",
       "      <th>GINI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Happy State Index</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.442629</td>\n",
       "      <td>0.804238</td>\n",
       "      <td>0.568716</td>\n",
       "      <td>0.684095</td>\n",
       "      <td>0.248354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP 2018</th>\n",
       "      <td>0.442629</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.569999</td>\n",
       "      <td>0.017127</td>\n",
       "      <td>0.596209</td>\n",
       "      <td>0.105938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life Expectancy</th>\n",
       "      <td>0.804238</td>\n",
       "      <td>0.569999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.402746</td>\n",
       "      <td>0.861436</td>\n",
       "      <td>0.249386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employment</th>\n",
       "      <td>0.568716</td>\n",
       "      <td>0.017127</td>\n",
       "      <td>0.402746</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.465914</td>\n",
       "      <td>0.247928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social Progress Index</th>\n",
       "      <td>0.684095</td>\n",
       "      <td>0.596209</td>\n",
       "      <td>0.861436</td>\n",
       "      <td>0.465914</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.261912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GINI</th>\n",
       "      <td>0.248354</td>\n",
       "      <td>0.105938</td>\n",
       "      <td>0.249386</td>\n",
       "      <td>0.247928</td>\n",
       "      <td>0.261912</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Happy State Index  GDP 2018  Life Expectancy  \\\n",
       "Happy State Index               1.000000  0.442629         0.804238   \n",
       "GDP 2018                        0.442629  1.000000         0.569999   \n",
       "Life Expectancy                 0.804238  0.569999         1.000000   \n",
       "Employment                      0.568716  0.017127         0.402746   \n",
       "Social Progress Index           0.684095  0.596209         0.861436   \n",
       "GINI                            0.248354  0.105938         0.249386   \n",
       "\n",
       "                       Employment  Social Progress Index      GINI  \n",
       "Happy State Index        0.568716               0.684095  0.248354  \n",
       "GDP 2018                 0.017127               0.596209  0.105938  \n",
       "Life Expectancy          0.402746               0.861436  0.249386  \n",
       "Employment               1.000000               0.465914  0.247928  \n",
       "Social Progress Index    0.465914               1.000000  0.261912  \n",
       "GINI                     0.247928               0.261912  1.000000  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bvT5qOKgD-go",
    "outputId": "e671e67f-14a1-469f-88cd-641f79d07f00"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Code</th>\n",
       "      <th>Happy State Index</th>\n",
       "      <th>GDP 2018</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Social Progress Index</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>HI</td>\n",
       "      <td>68.27</td>\n",
       "      <td>51.28</td>\n",
       "      <td>81.3</td>\n",
       "      <td>97.6</td>\n",
       "      <td>49.82</td>\n",
       "      <td>0.5580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Utah</td>\n",
       "      <td>UT</td>\n",
       "      <td>67.84</td>\n",
       "      <td>44.64</td>\n",
       "      <td>80.2</td>\n",
       "      <td>96.9</td>\n",
       "      <td>50.58</td>\n",
       "      <td>0.5937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>67.26</td>\n",
       "      <td>53.70</td>\n",
       "      <td>81.1</td>\n",
       "      <td>97.1</td>\n",
       "      <td>62.30</td>\n",
       "      <td>0.5504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>65.62</td>\n",
       "      <td>62.84</td>\n",
       "      <td>79.5</td>\n",
       "      <td>97.4</td>\n",
       "      <td>53.14</td>\n",
       "      <td>0.5467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>63.14</td>\n",
       "      <td>53.99</td>\n",
       "      <td>80.8</td>\n",
       "      <td>95.8</td>\n",
       "      <td>45.53</td>\n",
       "      <td>0.5101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          State Code  Happy State Index  GDP 2018  Life Expectancy  \\\n",
       "0        Hawaii   HI              68.27     51.28             81.3   \n",
       "1          Utah   UT              67.84     44.64             80.2   \n",
       "2     Minnesota   MN              67.26     53.70             81.1   \n",
       "3  North Dakota   ND              65.62     62.84             79.5   \n",
       "4    California   CA              63.14     53.99             80.8   \n",
       "\n",
       "   Employment  Social Progress Index    GINI  \n",
       "0        97.6                  49.82  0.5580  \n",
       "1        96.9                  50.58  0.5937  \n",
       "2        97.1                  62.30  0.5504  \n",
       "3        97.4                  53.14  0.5467  \n",
       "4        95.8                  45.53  0.5101  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each parameter was normalize onto a scale 0-100 in order for the final index to be scaled appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OYWkLzrLD-gs"
   },
   "outputs": [],
   "source": [
    "for param in df.columns[3:8]:\n",
    "    col_name = param + ' Norm' # add \"Norm\" for normalized\n",
    "    df[col_name] = ( df[param] - min(df[param]) ) / ( max(df[param]) - min(df[param]) ) * 100\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SCCPA7PND-gu",
    "outputId": "55124375-2b77-43e0-a8ea-6beb2d8070c3"
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Code</th>\n",
       "      <th>Happy State Index</th>\n",
       "      <th>GDP 2018</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Social Progress Index</th>\n",
       "      <th>GINI</th>\n",
       "      <th>GDP 2018 Norm</th>\n",
       "      <th>Life Expectancy Norm</th>\n",
       "      <th>Employment Norm</th>\n",
       "      <th>Social Progress Index Norm</th>\n",
       "      <th>GINI Norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>HI</td>\n",
       "      <td>68.27</td>\n",
       "      <td>51.28</td>\n",
       "      <td>81.3</td>\n",
       "      <td>97.6</td>\n",
       "      <td>49.82</td>\n",
       "      <td>0.5580</td>\n",
       "      <td>57.618058</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>60.148778</td>\n",
       "      <td>69.382504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Utah</td>\n",
       "      <td>UT</td>\n",
       "      <td>67.84</td>\n",
       "      <td>44.64</td>\n",
       "      <td>80.2</td>\n",
       "      <td>96.9</td>\n",
       "      <td>50.58</td>\n",
       "      <td>0.5937</td>\n",
       "      <td>37.897238</td>\n",
       "      <td>82.539683</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>62.167906</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>67.26</td>\n",
       "      <td>53.70</td>\n",
       "      <td>81.1</td>\n",
       "      <td>97.1</td>\n",
       "      <td>62.30</td>\n",
       "      <td>0.5504</td>\n",
       "      <td>64.805465</td>\n",
       "      <td>96.825397</td>\n",
       "      <td>88.095238</td>\n",
       "      <td>93.304995</td>\n",
       "      <td>62.864494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>65.62</td>\n",
       "      <td>62.84</td>\n",
       "      <td>79.5</td>\n",
       "      <td>97.4</td>\n",
       "      <td>53.14</td>\n",
       "      <td>0.5467</td>\n",
       "      <td>91.951292</td>\n",
       "      <td>71.428571</td>\n",
       "      <td>95.238095</td>\n",
       "      <td>68.969182</td>\n",
       "      <td>59.691252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>63.14</td>\n",
       "      <td>53.99</td>\n",
       "      <td>80.8</td>\n",
       "      <td>95.8</td>\n",
       "      <td>45.53</td>\n",
       "      <td>0.5101</td>\n",
       "      <td>65.666766</td>\n",
       "      <td>92.063492</td>\n",
       "      <td>57.142857</td>\n",
       "      <td>48.751328</td>\n",
       "      <td>28.301887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          State Code  Happy State Index  GDP 2018  Life Expectancy  \\\n",
       "0        Hawaii   HI              68.27     51.28             81.3   \n",
       "1          Utah   UT              67.84     44.64             80.2   \n",
       "2     Minnesota   MN              67.26     53.70             81.1   \n",
       "3  North Dakota   ND              65.62     62.84             79.5   \n",
       "4    California   CA              63.14     53.99             80.8   \n",
       "\n",
       "   Employment  Social Progress Index    GINI  GDP 2018 Norm  \\\n",
       "0        97.6                  49.82  0.5580      57.618058   \n",
       "1        96.9                  50.58  0.5937      37.897238   \n",
       "2        97.1                  62.30  0.5504      64.805465   \n",
       "3        97.4                  53.14  0.5467      91.951292   \n",
       "4        95.8                  45.53  0.5101      65.666766   \n",
       "\n",
       "   Life Expectancy Norm  Employment Norm  Social Progress Index Norm  \\\n",
       "0            100.000000       100.000000                   60.148778   \n",
       "1             82.539683        83.333333                   62.167906   \n",
       "2             96.825397        88.095238                   93.304995   \n",
       "3             71.428571        95.238095                   68.969182   \n",
       "4             92.063492        57.142857                   48.751328   \n",
       "\n",
       "    GINI Norm  \n",
       "0   69.382504  \n",
       "1  100.000000  \n",
       "2   62.864494  \n",
       "3   59.691252  \n",
       "4   28.301887  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Our_Index = A * GDP + B * Life_Expectancy + C * Employment + D * Social_Progress + E * Gini_Coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mSfMtLV7D-gy"
   },
   "outputs": [],
   "source": [
    "df['Our Index'] = df['GDP 2018 Norm'] * norm[0] + df['Life Expectancy Norm'] * norm[1] + df['Employment Norm'] * norm[2] + \\\n",
    "              df['Social Progress Index Norm'] * norm[3] + df['GINI Norm'] * norm[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qb0uHt4cD-gz",
    "outputId": "f408091b-d9b9-40ac-9464-03663110a2df"
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Code</th>\n",
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       "      <th>GDP 2018</th>\n",
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       "      <th>Our Index</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>HI</td>\n",
       "      <td>68.27</td>\n",
       "      <td>51.28</td>\n",
       "      <td>81.3</td>\n",
       "      <td>97.6</td>\n",
       "      <td>49.82</td>\n",
       "      <td>0.5580</td>\n",
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       "      <td>100.000000</td>\n",
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       "      <td>69.382504</td>\n",
       "      <td>82.992465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Utah</td>\n",
       "      <td>UT</td>\n",
       "      <td>67.84</td>\n",
       "      <td>44.64</td>\n",
       "      <td>80.2</td>\n",
       "      <td>96.9</td>\n",
       "      <td>50.58</td>\n",
       "      <td>0.5937</td>\n",
       "      <td>37.897238</td>\n",
       "      <td>82.539683</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>62.167906</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>72.546652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>67.26</td>\n",
       "      <td>53.70</td>\n",
       "      <td>81.1</td>\n",
       "      <td>97.1</td>\n",
       "      <td>62.30</td>\n",
       "      <td>0.5504</td>\n",
       "      <td>64.805465</td>\n",
       "      <td>96.825397</td>\n",
       "      <td>88.095238</td>\n",
       "      <td>93.304995</td>\n",
       "      <td>62.864494</td>\n",
       "      <td>89.254307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>65.62</td>\n",
       "      <td>62.84</td>\n",
       "      <td>79.5</td>\n",
       "      <td>97.4</td>\n",
       "      <td>53.14</td>\n",
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       "      <td>91.951292</td>\n",
       "      <td>71.428571</td>\n",
       "      <td>95.238095</td>\n",
       "      <td>68.969182</td>\n",
       "      <td>59.691252</td>\n",
       "      <td>77.235083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>63.14</td>\n",
       "      <td>53.99</td>\n",
       "      <td>80.8</td>\n",
       "      <td>95.8</td>\n",
       "      <td>45.53</td>\n",
       "      <td>0.5101</td>\n",
       "      <td>65.666766</td>\n",
       "      <td>92.063492</td>\n",
       "      <td>57.142857</td>\n",
       "      <td>48.751328</td>\n",
       "      <td>28.301887</td>\n",
       "      <td>68.081853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          State Code  Happy State Index  GDP 2018  Life Expectancy  \\\n",
       "0        Hawaii   HI              68.27     51.28             81.3   \n",
       "1          Utah   UT              67.84     44.64             80.2   \n",
       "2     Minnesota   MN              67.26     53.70             81.1   \n",
       "3  North Dakota   ND              65.62     62.84             79.5   \n",
       "4    California   CA              63.14     53.99             80.8   \n",
       "\n",
       "   Employment  Social Progress Index    GINI  GDP 2018 Norm  \\\n",
       "0        97.6                  49.82  0.5580      57.618058   \n",
       "1        96.9                  50.58  0.5937      37.897238   \n",
       "2        97.1                  62.30  0.5504      64.805465   \n",
       "3        97.4                  53.14  0.5467      91.951292   \n",
       "4        95.8                  45.53  0.5101      65.666766   \n",
       "\n",
       "   Life Expectancy Norm  Employment Norm  Social Progress Index Norm  \\\n",
       "0            100.000000       100.000000                   60.148778   \n",
       "1             82.539683        83.333333                   62.167906   \n",
       "2             96.825397        88.095238                   93.304995   \n",
       "3             71.428571        95.238095                   68.969182   \n",
       "4             92.063492        57.142857                   48.751328   \n",
       "\n",
       "    GINI Norm  Our Index  \n",
       "0   69.382504  82.992465  \n",
       "1  100.000000  72.546652  \n",
       "2   62.864494  89.254307  \n",
       "3   59.691252  77.235083  \n",
       "4   28.301887  68.081853  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UqvUSzydD-g1",
    "outputId": "03daee01-8127-4696-abb2-51d18b20f20e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.654\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Our Index'], df['Happy State Index'])\n",
    "plt.title('Our Index vs Happy State Index')\n",
    "plt.ylabel('Happy State Index (0-100)')\n",
    "plt.xlabel('Our Index (0-100)')\n",
    "\n",
    "X = np.array(df['Our Index']).reshape((-1, 1))\n",
    "y = df['Happy State Index']\n",
    "\n",
    "model = LinearRegression().fit(X, y)\n",
    "r_sq_gini = model.score(X, y)\n",
    "print(round(r_sq_gini, 3))\n",
    "\n",
    "y_pred = model.predict(X)\n",
    "plt.plot(X, y_pred, color='red', linewidth=1.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is r^2, this represents the correlation between Our Index and the Happy State Index which was used as a benchmark. 0.65 can be called \"moderately correlated\", although < 0.75."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yQBrbcQeD-g6",
    "outputId": "8b50acbc-2e58-44bc-8650-bc495909ab67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.654\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Our Index'], df['Happy State Index'])\n",
    "plt.title('Our Index vs Happy State Index')\n",
    "plt.ylabel('Happy State Index (0-100)')\n",
    "plt.xlabel('Uniform Index (0-100)')\n",
    "\n",
    "X = np.array(df['Our Index']).reshape((-1, 1))\n",
    "y = df['Happy State Index']\n",
    "\n",
    "model = LinearRegression().fit(X, y)\n",
    "r_sq_gini = model.score(X, y)\n",
    "print(round(r_sq_gini, 3))\n",
    "\n",
    "y_pred = model.predict(X)\n",
    "plt.plot(X, y_pred, color='red', linewidth=1.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns had to be renamed without whitespaces for OLS model fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BS1_zDWXD-g8"
   },
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Happy State Index': 'HSI', 'Our Index': 'OI'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Eq-E4ssiD-g9",
    "outputId": "3d39164e-8f79-41bb-8596-a413ca056164"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Code</th>\n",
       "      <th>HSI</th>\n",
       "      <th>GDP 2018</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Social Progress Index</th>\n",
       "      <th>GINI</th>\n",
       "      <th>GDP 2018 Norm</th>\n",
       "      <th>Life Expectancy Norm</th>\n",
       "      <th>Employment Norm</th>\n",
       "      <th>Social Progress Index Norm</th>\n",
       "      <th>GINI Norm</th>\n",
       "      <th>OI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>HI</td>\n",
       "      <td>68.27</td>\n",
       "      <td>51.28</td>\n",
       "      <td>81.3</td>\n",
       "      <td>97.6</td>\n",
       "      <td>49.82</td>\n",
       "      <td>0.5580</td>\n",
       "      <td>57.618058</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>60.148778</td>\n",
       "      <td>69.382504</td>\n",
       "      <td>82.992465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Utah</td>\n",
       "      <td>UT</td>\n",
       "      <td>67.84</td>\n",
       "      <td>44.64</td>\n",
       "      <td>80.2</td>\n",
       "      <td>96.9</td>\n",
       "      <td>50.58</td>\n",
       "      <td>0.5937</td>\n",
       "      <td>37.897238</td>\n",
       "      <td>82.539683</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>62.167906</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>72.546652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>67.26</td>\n",
       "      <td>53.70</td>\n",
       "      <td>81.1</td>\n",
       "      <td>97.1</td>\n",
       "      <td>62.30</td>\n",
       "      <td>0.5504</td>\n",
       "      <td>64.805465</td>\n",
       "      <td>96.825397</td>\n",
       "      <td>88.095238</td>\n",
       "      <td>93.304995</td>\n",
       "      <td>62.864494</td>\n",
       "      <td>89.254307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>65.62</td>\n",
       "      <td>62.84</td>\n",
       "      <td>79.5</td>\n",
       "      <td>97.4</td>\n",
       "      <td>53.14</td>\n",
       "      <td>0.5467</td>\n",
       "      <td>91.951292</td>\n",
       "      <td>71.428571</td>\n",
       "      <td>95.238095</td>\n",
       "      <td>68.969182</td>\n",
       "      <td>59.691252</td>\n",
       "      <td>77.235083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>63.14</td>\n",
       "      <td>53.99</td>\n",
       "      <td>80.8</td>\n",
       "      <td>95.8</td>\n",
       "      <td>45.53</td>\n",
       "      <td>0.5101</td>\n",
       "      <td>65.666766</td>\n",
       "      <td>92.063492</td>\n",
       "      <td>57.142857</td>\n",
       "      <td>48.751328</td>\n",
       "      <td>28.301887</td>\n",
       "      <td>68.081853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          State Code    HSI  GDP 2018  Life Expectancy  Employment  \\\n",
       "0        Hawaii   HI  68.27     51.28             81.3        97.6   \n",
       "1          Utah   UT  67.84     44.64             80.2        96.9   \n",
       "2     Minnesota   MN  67.26     53.70             81.1        97.1   \n",
       "3  North Dakota   ND  65.62     62.84             79.5        97.4   \n",
       "4    California   CA  63.14     53.99             80.8        95.8   \n",
       "\n",
       "   Social Progress Index    GINI  GDP 2018 Norm  Life Expectancy Norm  \\\n",
       "0                  49.82  0.5580      57.618058            100.000000   \n",
       "1                  50.58  0.5937      37.897238             82.539683   \n",
       "2                  62.30  0.5504      64.805465             96.825397   \n",
       "3                  53.14  0.5467      91.951292             71.428571   \n",
       "4                  45.53  0.5101      65.666766             92.063492   \n",
       "\n",
       "   Employment Norm  Social Progress Index Norm   GINI Norm         OI  \n",
       "0       100.000000                   60.148778   69.382504  82.992465  \n",
       "1        83.333333                   62.167906  100.000000  72.546652  \n",
       "2        88.095238                   93.304995   62.864494  89.254307  \n",
       "3        95.238095                   68.969182   59.691252  77.235083  \n",
       "4        57.142857                   48.751328   28.301887  68.081853  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6bo9mbYXD-g-"
   },
   "outputs": [],
   "source": [
    "outcome, predictors = patsy.dmatrices('HSI ~ OI', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mCmrAcOfD-g_"
   },
   "outputs": [],
   "source": [
    "OLS_model = sm.OLS(outcome, predictors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is a summary of the Ordinary Least Squares model, the most useful statistic is the r^2 value in the upper right corner. \n",
    "\n",
    "### R^2 = 0.654"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hfZ_8qbXD-g_",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    HSI   R-squared:                       0.654\n",
      "Model:                            OLS   Adj. R-squared:                  0.647\n",
      "Method:                 Least Squares   F-statistic:                     90.91\n",
      "Date:                Fri, 14 Jun 2019   Prob (F-statistic):           1.18e-12\n",
      "Time:                        10:48:42   Log-Likelihood:                -151.37\n",
      "No. Observations:                  50   AIC:                             306.7\n",
      "Df Residuals:                      48   BIC:                             310.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     32.0860      2.215     14.488      0.000      27.633      36.539\n",
      "OI             0.3450      0.036      9.535      0.000       0.272       0.418\n",
      "==============================================================================\n",
      "Omnibus:                        0.048   Durbin-Watson:                   1.127\n",
      "Prob(Omnibus):                  0.976   Jarque-Bera (JB):                0.064\n",
      "Skew:                          -0.046   Prob(JB):                        0.969\n",
      "Kurtosis:                       2.851   Cond. No.                         188.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "res = OLS_model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This concludes the data analysis section. As seen above, we have to reject our hypothesis. A more thorough explanation of the implications of this will be provided in the conclusion section below. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nG7GuPYfIJ-X"
   },
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "psoy1OXRIRxd"
   },
   "source": [
    "Ethical Considerations:\n",
    "-\tWhether any information that can identify a person is in our data, keep everything anonymous.\n",
    "-\tData, unless publicly available, will only be obtained with owners’ consent.\n",
    "-\tData collected will be analyzed for author biases.\n",
    "-\tThe research will be carried out in a manner that does not cause any undue harm to anyone.\n",
    "-\tThe process and/or findings of the present research will not sere to propagate bias against any individual, or group.\n",
    "-\tAnalysis will be conducted in a manner that symbolizes pure honesty and ensures data anonymity. \n",
    "-\tAnalysis will be documented well enough to be reproduceable in future events.\n",
    "-\tMetric Selection will be charged thoughtfully eliminating personal bias toward the selections.\n",
    "-\tExplanation of models features, and justifications will be understandable and clear.\n",
    "\n",
    "With these bullet points in mind; our team designed a way to minimize selection-bias from each of us by brainstorming dozens of ideas in regard to supporting our hypothesis, and placing them in a random-selection tool. Before picking the variables, we were to research on, each of our team mates had to agree that each variable was equally valid and weighted the same conceptual importance before searching for the datasets. \n",
    "\n",
    "After picking a few randomized variable concepts relating to our hypothesis; we began our search for datasets and found sets that did not include any personal information. Beyond this, we checked the sources of each dataset to make sure that the pieces of data could not reverse-engineer the location or personal information of any individual or group of individuals. \n",
    "With these data sets free of personal-information we then checked for author biases and tried to use data sets that were open-sourced and were being constantly monitored by the site-publishers artificial-intelligence. Although it is not a real AI, these AI sentries provide diligent and usually an un-bias security in regard to defending data-sets from malicious or false alterations. \n",
    "\n",
    "After cleaning up the data sets, we ambitiously tried to implement security measures to mitigate against unwanted users from altering the Jupyter Notebook. Unfortunately, it ended up not being as secured as we would have hoped, causing us to scrap our efforts in providing security for the code. With that said, the models generated have no personal data and the features are rendered from imported Python Modules. This means that, if a user were to alter the code; they would still be subject to using the public module-functions in order to hide un-desired alterations to our models. \n",
    "For the metric selection, since we had randomly chosen the data sets from equally weighted concepts, we already ensured that the metric selection did no contain biases from our team mates. Although we selected the columns to include, it was to ensure the relevancy to answering our hypothesis.\n",
    "\n",
    "Lastly, in our explanation of the models, we each re-read the explanations which provided multi-feedback loops to ensure that we weren’t targeting any individual or group with the features described in our models. By having a system of checks, we did our best to ensure the privacy of individuals was safe and that the models generated from our analysis were not to harm any individual nor group.\n",
    " \n",
    "After completion of this hypothesis, we discussed possible un-predictable harms that might arise from our models. The only one that came to our minds would be hurt feelings from the states that ranked lower on our “Happy State Index”.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "atFTbeKpIR6c"
   },
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UPbzl59lIZge"
   },
   "source": [
    "Our attempt to devise a Happiness Index of our own, produced by metrics such as GDP per capita, life expectancy, employment, social progress and Gini coefficient, reasonably encompasses a wide range of socio-economic that may ‘define’ happiness across the 50 states. \n",
    "\n",
    "By conducting a number of linear regressions on each metric with the Happy State Index data, we were able to ascertain the extent to which each metric affects the overall score. These R-squared values were taken as ‘weights’ that led to the creation of our own happiness index formula. Later, as per our hypothesis, we conducted a regression of the newly devised happiness index with the Happy State Index to see how closely their respective values matched for each state. \n",
    "\n",
    "Eventually, an R-squared value of 0.65 wasn’t too short of the hypothesized value of 0.75, and hence doesn’t really discredit the utility of our own index. This leads us to believe that our attempts to quantify happiness using our own metrics gave us a reasonably close value to the one used to calculate the Happy State Index yet leaving room for improvement to consider more variables that can lead to a more nuanced definition of happiness.   "
   ]
  }
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