Virginia_Life_Expectancy_ML.ipynb

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        "#Predicting Life Expectancy Based on Virginia Health Opportunity Index\n",
        "---\n",
        "###Bryant Chow, Josh De Vera, Tony Le\n",
        "###CS 4774\n",
        "###Professor Nguyen\n",
        "###Guiding TA: Emery Shelly\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
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      "source": [
        "## Project Abstract\n",
        "The purpose of this experiment is to use the Health Opportunity Index to predict life expectancy and identify which socio-economic factors as well as public facilities affect the overall life expectancy within VA. We can then use this data to 1) help areas with lower life expectancies and 2) see which features affect life expectancy the most to guide public policy. There have already been a few projects which have tackled this, but what makes our project different is our second point. The National Institute of Health (NIH) has done a similar study which looks at Ohio and tests the validity of the Health Opportunity Index (HOI) and its ability to predict life expectancy. It found that the HOI has a strong correlation with life expectancy. They tested their model on the following years’ HOI and it was able to accurately predict the life expectancy of a county. We can use similar techniques and compare our Virginia data set error to that of Ohio to make sure our model works accurately. One limitation of the Ohio data set was the lack of data from rural areas, but the data set we found has a considerable amount of data on rural areas so we can try to make an even more accurate model with data from Virginia’s rural counties.\n"
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        "# Data Sets Combined fot this project:\n",
        "\n",
        "VA Health Opportunity Index: https://data.virginia.gov/Family-Health/Health-Opportunity-Index/6q6u-dcz7\n",
        "\n",
        "VA Life Expectancy:\n",
        "https://data.virginia.gov/Health/Open-View-VA-Census-Tract-Life-Expectancy/pdsy-56sx\n",
        "\n",
        "US Census ID Forms:\n",
        "https://www.fcc.gov/general/form-477-census-tract-information\n"
      ],
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      "source": [
        "# Data Set Merging\n",
        "To create the single data set for this project, I used the US Census ID dataset and cross-referenced it with the Va Life Expectancy data set since each data point was identified with their Census ID Number. Then, I cross-referenced that data with the VA Health Opportunity Index Dataset using the Census Tract Number which isn't on the US Census ID dataset. The final dataset contains the each counties Census Tract number, the features that are accounted in the Health Opportunity Index, the Health Opportunity Index score, and the life expectancy.\n"
      ],
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      "source": [
        "import numpy as np"
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      "source": [
        "import pandas as pd\n",
        "\n",
        "hoi = pd.read_csv('Health_Opportunity_Index.csv')\n",
        "lifeExp = pd.read_csv('Open_View_-_VA_Census_Tract_Life_Expectancy.csv')"
      ],
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        "hoi.head()"
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              "   Census Tract Rural~Urban  Access to Care  Employment Accessibility  \\\n",
              "0   51650011400       Urban        0.486042                  0.015251   \n",
              "1   51760040300       Urban        0.502987                  0.042804   \n",
              "2   51013101701       Urban        0.432198                  0.131224   \n",
              "3   51710002500       Urban        0.465814                  0.041683   \n",
              "4   51760040400       Urban        0.502987                  0.084305   \n",
              "\n",
              "   Affordability  Air Quality  Population Churning  Education  \\\n",
              "0       0.043043     0.730782             0.468193   0.630734   \n",
              "1       0.641642     0.351528             0.022901   0.871575   \n",
              "2       0.627628     0.800711             0.513995   0.853716   \n",
              "3       0.021021     0.754040             0.444020   0.697862   \n",
              "4       0.662663     0.382158             0.427481   0.800714   \n",
              "\n",
              "   Food Accessibility  Income Inequality  Job Participation  \\\n",
              "0            0.977517           0.034818              0.150   \n",
              "1            0.933302           0.082321              0.373   \n",
              "2            0.933302           0.395412              0.759   \n",
              "3            0.741324           0.258974              0.436   \n",
              "4            0.933302           0.000000              0.551   \n",
              "\n",
              "   Population Density  Segregation  Material Deprivation  Walkability  \\\n",
              "0            0.043334     0.770141              0.000000     0.245825   \n",
              "1            0.227476     0.639438              0.133458     0.307647   \n",
              "2            1.000000     0.801044              0.343863     0.731265   \n",
              "3            0.113065     0.919320              0.228861     0.321052   \n",
              "4            0.231267     0.822681              0.184284     0.326225   \n",
              "\n",
              "   Community Environment Profile  Consumer Opportunity Profile  \\\n",
              "0                       0.540012                      0.390787   \n",
              "1                       0.777319                      0.764695   \n",
              "2                       1.000000                      0.827284   \n",
              "3                       0.561143                      0.302549   \n",
              "4                       0.719178                      0.773519   \n",
              "\n",
              "   Economic Opportunity Profile  Wellness Disparity Profile  \\\n",
              "0                      0.006034                    0.409863   \n",
              "1                      0.033064                    0.488495   \n",
              "2                      0.326435                    0.355932   \n",
              "3                      0.183497                    0.319484   \n",
              "4                      0.075911                    0.395098   \n",
              "\n",
              "   Health Opportunity Index  \n",
              "0                  0.000000  \n",
              "1                  0.015558  \n",
              "2                  0.032861  \n",
              "3                  0.077873  \n",
              "4                  0.085121  "
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            "text/plain": [
              "      State         Locality   FIPS  Census Tract Number  Life Expectancy  \\\n",
              "0  Virginia          (blank)  51000                  NaN             79.4   \n",
              "1  Virginia  Accomack County  51001                901.0             74.5   \n",
              "2  Virginia  Accomack County  51001                902.0             77.5   \n",
              "3  Virginia  Accomack County  51001                903.0             76.1   \n",
              "4  Virginia  Accomack County  51001                904.0             77.7   \n",
              "\n",
              "   Life Expectancy Standard Error  \n",
              "0                          0.0250  \n",
              "1                          1.5145  \n",
              "2                          1.1577  \n",
              "3                          3.4158  \n",
              "4                          1.2010  "
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              "      <th>1</th>\n",
              "      <td>Virginia</td>\n",
              "      <td>Accomack County</td>\n",
              "      <td>51001</td>\n",
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              "      <th>2</th>\n",
              "      <td>Virginia</td>\n",
              "      <td>Accomack County</td>\n",
              "      <td>51001</td>\n",
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              "      <th>3</th>\n",
              "      <td>Virginia</td>\n",
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              "      <th>4</th>\n",
              "      <td>Virginia</td>\n",
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              "      <td>51001</td>\n",
              "      <td>904.0</td>\n",
              "      <td>77.7</td>\n",
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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Method to remove trailing 0 in tract number and remove the decimal to only leave the digits\n",
        "def convertTract(num):\n",
        "  numStr = str(num)\n",
        "  if numStr[-1] == \"0\":\n",
        "    numStr = numStr[0:len(numStr)-1]\n",
        "  numStr = numStr.replace('.','')\n",
        "  return numStr"
      ],
      "metadata": {
        "id": "yd56lNAIH0Zn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop any rows with NaN values and resets the index afterwards\n",
        "lifeExp = lifeExp.dropna()\n",
        "lifeExp = lifeExp.reset_index(drop = True)"
      ],
      "metadata": {
        "id": "tff9DDO1ItyQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creates a new column with the census tract digits as strings\n",
        "tempArray = []\n",
        "for i in range(len(lifeExp)):\n",
        "  s = convertTract(lifeExp['Census Tract Number'][i])\n",
        "  tempArray.append(s)\n",
        "\n",
        "lifeExp['Census Tract Number Digits'] = tempArray"
      ],
      "metadata": {
        "id": "uy2JV_dyKElq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creates a new column with the full FIPS codes\n",
        "tempArray = []\n",
        "for i in range(len(lifeExp)):\n",
        "  fips = str(lifeExp['FIPS'][i])\n",
        "  fips = fips + '000000'\n",
        "\n",
        "  fipsCode = int(fips) + int(lifeExp['Census Tract Number Digits'][i])\n",
        "  tempArray.append(fipsCode)\n",
        "\n",
        "lifeExp['Census Tract'] = tempArray"
      ],
      "metadata": {
        "id": "EAcuBkUSNUuC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Merge the 2 datasets based on the census tract fips codes they have in common\n",
        "fullData = pd.merge(hoi, lifeExp, on='Census Tract')"
      ],
      "metadata": {
        "id": "AtHZKDL-PoL2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fullData.drop(['FIPS','Census Tract Number','Life Expectancy Standard Error', 'Census Tract Number Digits'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "JUMsop3rVhFD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fullData"
      ],
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          "height": 731
        },
        "id": "O51czwPLP8WA",
        "outputId": "c47d87db-ee1e-48a7-84cd-0bb741356504"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Census Tract Rural~Urban  Access to Care  Employment Accessibility  \\\n",
              "0     51013101403       Urban        0.466760                  0.122853   \n",
              "1     51121020201       Urban        0.371854                  0.079464   \n",
              "2     51013101802       Urban        0.434747                  0.131422   \n",
              "3     51013101702       Urban        0.417921                  0.124969   \n",
              "4     51191010502       Urban        0.370109                  0.068883   \n",
              "..            ...         ...             ...                       ...   \n",
              "856   51550021011       Urban        0.389754                  0.122914   \n",
              "857   51059431002       Urban        0.500215                  0.142495   \n",
              "858   51153901233       Urban        0.653177                  0.123813   \n",
              "859   51153901005       Urban        0.661008                  0.140759   \n",
              "860   51085320803       Urban        0.475858                  0.131939   \n",
              "\n",
              "     Affordability  Air Quality  Population Churning  Education  \\\n",
              "0         0.660661     0.783266             0.582697   0.902432   \n",
              "1         0.192192     0.889298             0.432570   0.892324   \n",
              "2         0.708709     0.767906             0.659033   0.888723   \n",
              "3         0.678679     0.709216             0.676845   0.858297   \n",
              "4         0.182182     0.837608             0.685751   0.670669   \n",
              "..             ...          ...                  ...        ...   \n",
              "856       0.690691     0.943812             0.912214   0.808100   \n",
              "857       0.693694     0.921067             0.949109   0.820499   \n",
              "858       0.707708     0.875726             0.909669   0.792357   \n",
              "859       0.681682     0.793776             0.891858   0.824851   \n",
              "860       0.725726     0.919054             0.930025   0.819782   \n",
              "\n",
              "     Food Accessibility  Income Inequality  ...  Material Deprivation  \\\n",
              "0              0.933302           0.475169  ...              0.386328   \n",
              "1              0.996297           0.251282  ...              0.284302   \n",
              "2              0.933302           0.475709  ...              0.302523   \n",
              "3              0.933302           0.336572  ...              0.244075   \n",
              "4              0.942139           0.256410  ...              0.299769   \n",
              "..                  ...                ...  ...                   ...   \n",
              "856            0.980990           0.624696  ...              0.622355   \n",
              "857            0.933302           0.660054  ...              0.615473   \n",
              "858            0.963917           0.613900  ...              0.709124   \n",
              "859            0.982599           0.636707  ...              0.692808   \n",
              "860            0.982576           0.588394  ...              0.735580   \n",
              "\n",
              "     Walkability  Community Environment Profile  Consumer Opportunity Profile  \\\n",
              "0       0.966011                       0.979486                      0.880676   \n",
              "1       0.233945                       0.541058                      0.609358   \n",
              "2       0.598746                       0.837732                      0.828389   \n",
              "3       0.656096                       0.751125                      0.789080   \n",
              "4       0.153523                       0.407921                      0.453114   \n",
              "..           ...                            ...                           ...   \n",
              "856     0.162683                       0.239709                      0.724436   \n",
              "857     0.143828                       0.270243                      0.710679   \n",
              "858     0.148771                       0.249682                      0.759295   \n",
              "859     0.124491                       0.276896                      0.766127   \n",
              "860     0.168913                       0.244246                      0.788192   \n",
              "\n",
              "     Economic Opportunity Profile  Wellness Disparity Profile  \\\n",
              "0                        0.346453                    0.370990   \n",
              "1                        0.177157                    0.281539   \n",
              "2                        0.361443                    0.375004   \n",
              "3                        0.291615                    0.344019   \n",
              "4                        0.174846                    0.235938   \n",
              "..                            ...                         ...   \n",
              "856                      0.360059                    0.590709   \n",
              "857                      0.420954                    0.531661   \n",
              "858                      0.372530                    0.621149   \n",
              "859                      0.390717                    0.601810   \n",
              "860                      0.373881                    0.561809   \n",
              "\n",
              "     Health Opportunity Index     State               Locality  \\\n",
              "0                    0.091417  Virginia       Arlington County   \n",
              "1                    0.167349  Virginia      Montgomery County   \n",
              "2                    0.191628  Virginia       Arlington County   \n",
              "3                    0.200976  Virginia       Arlington County   \n",
              "4                    0.204015  Virginia      Washington County   \n",
              "..                        ...       ...                    ...   \n",
              "856                  0.613450  Virginia     City of Chesapeake   \n",
              "857                  0.625536  Virginia         Fairfax County   \n",
              "858                  0.631906  Virginia  Prince William County   \n",
              "859                  0.634309  Virginia  Prince William County   \n",
              "860                  0.638278  Virginia         Hanover County   \n",
              "\n",
              "     Life Expectancy  \n",
              "0               80.7  \n",
              "1               78.9  \n",
              "2               80.9  \n",
              "3               80.9  \n",
              "4               72.7  \n",
              "..               ...  \n",
              "856             79.5  \n",
              "857             80.9  \n",
              "858             79.8  \n",
              "859             80.4  \n",
              "860             76.4  \n",
              "\n",
              "[861 rows x 23 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Census Tract</th>\n",
              "      <th>Rural~Urban</th>\n",
              "      <th>Access to Care</th>\n",
              "      <th>Employment Accessibility</th>\n",
              "      <th>Affordability</th>\n",
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              "      <th>Education</th>\n",
              "      <th>Food Accessibility</th>\n",
              "      <th>Income Inequality</th>\n",
              "      <th>...</th>\n",
              "      <th>Material Deprivation</th>\n",
              "      <th>Walkability</th>\n",
              "      <th>Community Environment Profile</th>\n",
              "      <th>Consumer Opportunity Profile</th>\n",
              "      <th>Economic Opportunity Profile</th>\n",
              "      <th>Wellness Disparity Profile</th>\n",
              "      <th>Health Opportunity Index</th>\n",
              "      <th>State</th>\n",
              "      <th>Locality</th>\n",
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              "      <th>0</th>\n",
              "      <td>51013101403</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0.466760</td>\n",
              "      <td>0.122853</td>\n",
              "      <td>0.660661</td>\n",
              "      <td>0.783266</td>\n",
              "      <td>0.582697</td>\n",
              "      <td>0.902432</td>\n",
              "      <td>0.933302</td>\n",
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              "      <td>0.386328</td>\n",
              "      <td>0.966011</td>\n",
              "      <td>0.979486</td>\n",
              "      <td>0.880676</td>\n",
              "      <td>0.346453</td>\n",
              "      <td>0.370990</td>\n",
              "      <td>0.091417</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>Arlington County</td>\n",
              "      <td>80.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>51121020201</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0.371854</td>\n",
              "      <td>0.079464</td>\n",
              "      <td>0.192192</td>\n",
              "      <td>0.889298</td>\n",
              "      <td>0.432570</td>\n",
              "      <td>0.892324</td>\n",
              "      <td>0.996297</td>\n",
              "      <td>0.251282</td>\n",
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              "      <td>0.284302</td>\n",
              "      <td>0.233945</td>\n",
              "      <td>0.541058</td>\n",
              "      <td>0.609358</td>\n",
              "      <td>0.177157</td>\n",
              "      <td>0.281539</td>\n",
              "      <td>0.167349</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>Montgomery County</td>\n",
              "      <td>78.9</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>51013101802</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0.434747</td>\n",
              "      <td>0.131422</td>\n",
              "      <td>0.708709</td>\n",
              "      <td>0.767906</td>\n",
              "      <td>0.659033</td>\n",
              "      <td>0.888723</td>\n",
              "      <td>0.933302</td>\n",
              "      <td>0.475709</td>\n",
              "      <td>...</td>\n",
              "      <td>0.302523</td>\n",
              "      <td>0.598746</td>\n",
              "      <td>0.837732</td>\n",
              "      <td>0.828389</td>\n",
              "      <td>0.361443</td>\n",
              "      <td>0.375004</td>\n",
              "      <td>0.191628</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>Arlington County</td>\n",
              "      <td>80.9</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>51013101702</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0.417921</td>\n",
              "      <td>0.124969</td>\n",
              "      <td>0.678679</td>\n",
              "      <td>0.709216</td>\n",
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              "      <td>0.933302</td>\n",
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              "      <td>0.291615</td>\n",
              "      <td>0.344019</td>\n",
              "      <td>0.200976</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>Arlington County</td>\n",
              "      <td>80.9</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>51191010502</td>\n",
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              "      <td>0.370109</td>\n",
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              "      <td>0.182182</td>\n",
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              "      <td>Virginia</td>\n",
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              "      <td>0.360059</td>\n",
              "      <td>0.590709</td>\n",
              "      <td>0.613450</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>City of Chesapeake</td>\n",
              "      <td>79.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>857</th>\n",
              "      <td>51059431002</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0.500215</td>\n",
              "      <td>0.142495</td>\n",
              "      <td>0.693694</td>\n",
              "      <td>0.921067</td>\n",
              "      <td>0.949109</td>\n",
              "      <td>0.820499</td>\n",
              "      <td>0.933302</td>\n",
              "      <td>0.660054</td>\n",
              "      <td>...</td>\n",
              "      <td>0.615473</td>\n",
              "      <td>0.143828</td>\n",
              "      <td>0.270243</td>\n",
              "      <td>0.710679</td>\n",
              "      <td>0.420954</td>\n",
              "      <td>0.531661</td>\n",
              "      <td>0.625536</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>Fairfax County</td>\n",
              "      <td>80.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>858</th>\n",
              "      <td>51153901233</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0.653177</td>\n",
              "      <td>0.123813</td>\n",
              "      <td>0.707708</td>\n",
              "      <td>0.875726</td>\n",
              "      <td>0.909669</td>\n",
              "      <td>0.792357</td>\n",
              "      <td>0.963917</td>\n",
              "      <td>0.613900</td>\n",
              "      <td>...</td>\n",
              "      <td>0.709124</td>\n",
              "      <td>0.148771</td>\n",
              "      <td>0.249682</td>\n",
              "      <td>0.759295</td>\n",
              "      <td>0.372530</td>\n",
              "      <td>0.621149</td>\n",
              "      <td>0.631906</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>Prince William County</td>\n",
              "      <td>79.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>859</th>\n",
              "      <td>51153901005</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0.661008</td>\n",
              "      <td>0.140759</td>\n",
              "      <td>0.681682</td>\n",
              "      <td>0.793776</td>\n",
              "      <td>0.891858</td>\n",
              "      <td>0.824851</td>\n",
              "      <td>0.982599</td>\n",
              "      <td>0.636707</td>\n",
              "      <td>...</td>\n",
              "      <td>0.692808</td>\n",
              "      <td>0.124491</td>\n",
              "      <td>0.276896</td>\n",
              "      <td>0.766127</td>\n",
              "      <td>0.390717</td>\n",
              "      <td>0.601810</td>\n",
              "      <td>0.634309</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>Prince William County</td>\n",
              "      <td>80.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>860</th>\n",
              "      <td>51085320803</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0.475858</td>\n",
              "      <td>0.131939</td>\n",
              "      <td>0.725726</td>\n",
              "      <td>0.919054</td>\n",
              "      <td>0.930025</td>\n",
              "      <td>0.819782</td>\n",
              "      <td>0.982576</td>\n",
              "      <td>0.588394</td>\n",
              "      <td>...</td>\n",
              "      <td>0.735580</td>\n",
              "      <td>0.168913</td>\n",
              "      <td>0.244246</td>\n",
              "      <td>0.788192</td>\n",
              "      <td>0.373881</td>\n",
              "      <td>0.561809</td>\n",
              "      <td>0.638278</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>Hanover County</td>\n",
              "      <td>76.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>861 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91e21e34-961e-416f-8de8-37679657bd31')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-91e21e34-961e-416f-8de8-37679657bd31');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Here is the code for the data and actual project"
      ],
      "metadata": {
        "id": "NM191UIbiQbz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Crunched on time, heres the code to start a simple linear regression, we can organize it later"
      ],
      "metadata": {
        "id": "YKRpraTsdWR2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fullData.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "COTsuKcEihoT",
        "outputId": "1f6e0f55-be9d-43c8-bd58-2f147355edff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Census Tract  Access to Care  \\\n",
              "Census Tract                       1.000000       -0.101553   \n",
              "Access to Care                    -0.101553        1.000000   \n",
              "Employment Accessibility           0.217050        0.174927   \n",
              "Affordability                     -0.274574        0.102286   \n",
              "Air Quality                       -0.095797       -0.241885   \n",
              "Population Churning               -0.288376       -0.024942   \n",
              "Education                         -0.173096        0.155219   \n",
              "Food Accessibility                -0.059004        0.047246   \n",
              "Income Inequality                 -0.030739        0.009472   \n",
              "Job Participation                 -0.044878        0.181788   \n",
              "Population Density                 0.053454        0.099365   \n",
              "Segregation                       -0.028051        0.004721   \n",
              "Material Deprivation              -0.212761       -0.000611   \n",
              "Walkability                        0.286851        0.103322   \n",
              "Community Environment Profile      0.225648        0.133512   \n",
              "Consumer Opportunity Profile      -0.272304        0.153253   \n",
              "Economic Opportunity Profile       0.076913        0.149467   \n",
              "Wellness Disparity Profile        -0.054204        0.721690   \n",
              "Health Opportunity Index          -0.242221        0.258817   \n",
              "Life Expectancy                   -0.275855        0.127604   \n",
              "\n",
              "                               Employment Accessibility  Affordability  \\\n",
              "Census Tract                                   0.217050      -0.274574   \n",
              "Access to Care                                 0.174927       0.102286   \n",
              "Employment Accessibility                       1.000000       0.148547   \n",
              "Affordability                                  0.148547       1.000000   \n",
              "Air Quality                                   -0.205619       0.147145   \n",
              "Population Churning                           -0.124648       0.301555   \n",
              "Education                                      0.205088       0.720131   \n",
              "Food Accessibility                             0.040581       0.236850   \n",
              "Income Inequality                              0.315512       0.371452   \n",
              "Job Participation                              0.578782       0.323145   \n",
              "Population Density                             0.252585       0.078537   \n",
              "Segregation                                    0.054193       0.063837   \n",
              "Material Deprivation                           0.021448       0.607648   \n",
              "Walkability                                    0.313642       0.082831   \n",
              "Community Environment Profile                  0.271898      -0.132742   \n",
              "Consumer Opportunity Profile                   0.012975       0.849763   \n",
              "Economic Opportunity Profile                   0.742027       0.268344   \n",
              "Wellness Disparity Profile                     0.089301       0.029963   \n",
              "Health Opportunity Index                       0.233853       0.581156   \n",
              "Life Expectancy                                0.113947       0.646233   \n",
              "\n",
              "                               Air Quality  Population Churning  Education  \\\n",
              "Census Tract                     -0.095797            -0.288376  -0.173096   \n",
              "Access to Care                   -0.241885            -0.024942   0.155219   \n",
              "Employment Accessibility         -0.205619            -0.124648   0.205088   \n",
              "Affordability                     0.147145             0.301555   0.720131   \n",
              "Air Quality                       1.000000             0.237880  -0.001716   \n",
              "Population Churning               0.237880             1.000000   0.056493   \n",
              "Education                        -0.001716             0.056493   1.000000   \n",
              "Food Accessibility                0.030507             0.087803   0.222487   \n",
              "Income Inequality                 0.080957             0.140384   0.145495   \n",
              "Job Participation                -0.198527            -0.114245   0.283563   \n",
              "Population Density               -0.293198            -0.323118   0.200327   \n",
              "Segregation                      -0.000969             0.028417   0.039245   \n",
              "Material Deprivation              0.299885             0.508192   0.494881   \n",
              "Walkability                      -0.310749            -0.424650   0.315842   \n",
              "Community Environment Profile    -0.524040            -0.657965   0.151846   \n",
              "Consumer Opportunity Profile      0.094587             0.314793   0.850497   \n",
              "Economic Opportunity Profile     -0.003314            -0.009998   0.120426   \n",
              "Wellness Disparity Profile       -0.174685            -0.037753   0.085361   \n",
              "Health Opportunity Index          0.262918             0.586541   0.343976   \n",
              "Life Expectancy                   0.015143             0.158760   0.656399   \n",
              "\n",
              "                               Food Accessibility  Income Inequality  \\\n",
              "Census Tract                            -0.059004          -0.030739   \n",
              "Access to Care                           0.047246           0.009472   \n",
              "Employment Accessibility                 0.040581           0.315512   \n",
              "Affordability                            0.236850           0.371452   \n",
              "Air Quality                              0.030507           0.080957   \n",
              "Population Churning                      0.087803           0.140384   \n",
              "Education                                0.222487           0.145495   \n",
              "Food Accessibility                       1.000000           0.117935   \n",
              "Income Inequality                        0.117935           1.000000   \n",
              "Job Participation                        0.110582           0.409413   \n",
              "Population Density                       0.002299           0.030091   \n",
              "Segregation                              0.091104           0.043823   \n",
              "Material Deprivation                     0.196838           0.352046   \n",
              "Walkability                              0.063371          -0.028158   \n",
              "Community Environment Profile           -0.009147          -0.201656   \n",
              "Consumer Opportunity Profile             0.501222           0.157814   \n",
              "Economic Opportunity Profile            -0.061523           0.787819   \n",
              "Wellness Disparity Profile              -0.028813          -0.023469   \n",
              "Health Opportunity Index                 0.175768           0.554829   \n",
              "Life Expectancy                          0.237864           0.185906   \n",
              "\n",
              "                               Job Participation  Population Density  \\\n",
              "Census Tract                           -0.044878            0.053454   \n",
              "Access to Care                          0.181788            0.099365   \n",
              "Employment Accessibility                0.578782            0.252585   \n",
              "Affordability                           0.323145            0.078537   \n",
              "Air Quality                            -0.198527           -0.293198   \n",
              "Population Churning                    -0.114245           -0.323118   \n",
              "Education                               0.283563            0.200327   \n",
              "Food Accessibility                      0.110582            0.002299   \n",
              "Income Inequality                       0.409413            0.030091   \n",
              "Job Participation                       1.000000            0.386398   \n",
              "Population Density                      0.386398            1.000000   \n",
              "Segregation                             0.068581            0.048638   \n",
              "Material Deprivation                   -0.013987           -0.332270   \n",
              "Walkability                             0.353069            0.630983   \n",
              "Community Environment Profile           0.354718            0.796854   \n",
              "Consumer Opportunity Profile            0.179280            0.082231   \n",
              "Economic Opportunity Profile            0.743948            0.232876   \n",
              "Wellness Disparity Profile              0.084316            0.038368   \n",
              "Health Opportunity Index                0.230974           -0.412225   \n",
              "Life Expectancy                         0.237451            0.144265   \n",
              "\n",
              "                               Segregation  Material Deprivation  Walkability  \\\n",
              "Census Tract                     -0.028051             -0.212761     0.286851   \n",
              "Access to Care                    0.004721             -0.000611     0.103322   \n",
              "Employment Accessibility          0.054193              0.021448     0.313642   \n",
              "Affordability                     0.063837              0.607648     0.082831   \n",
              "Air Quality                      -0.000969              0.299885    -0.310749   \n",
              "Population Churning               0.028417              0.508192    -0.424650   \n",
              "Education                         0.039245              0.494881     0.315842   \n",
              "Food Accessibility                0.091104              0.196838     0.063371   \n",
              "Income Inequality                 0.043823              0.352046    -0.028158   \n",
              "Job Participation                 0.068581             -0.013987     0.353069   \n",
              "Population Density                0.048638             -0.332270     0.630983   \n",
              "Segregation                       1.000000              0.000044     0.010431   \n",
              "Material Deprivation              0.000044              1.000000    -0.282516   \n",
              "Walkability                       0.010431             -0.282516     1.000000   \n",
              "Community Environment Profile     0.162785             -0.590112     0.812265   \n",
              "Consumer Opportunity Profile      0.125292              0.683564     0.148133   \n",
              "Economic Opportunity Profile      0.071416              0.143508     0.185216   \n",
              "Wellness Disparity Profile       -0.688802             -0.000473     0.067683   \n",
              "Health Opportunity Index         -0.259205              0.760733    -0.381492   \n",
              "Life Expectancy                   0.044646              0.403099     0.129092   \n",
              "\n",
              "                               Community Environment Profile  \\\n",
              "Census Tract                                        0.225648   \n",
              "Access to Care                                      0.133512   \n",
              "Employment Accessibility                            0.271898   \n",
              "Affordability                                      -0.132742   \n",
              "Air Quality                                        -0.524040   \n",
              "Population Churning                                -0.657965   \n",
              "Education                                           0.151846   \n",
              "Food Accessibility                                 -0.009147   \n",
              "Income Inequality                                  -0.201656   \n",
              "Job Participation                                   0.354718   \n",
              "Population Density                                  0.796854   \n",
              "Segregation                                         0.162785   \n",
              "Material Deprivation                               -0.590112   \n",
              "Walkability                                         0.812265   \n",
              "Community Environment Profile                       1.000000   \n",
              "Consumer Opportunity Profile                       -0.071891   \n",
              "Economic Opportunity Profile                        0.076702   \n",
              "Wellness Disparity Profile                         -0.015893   \n",
              "Health Opportunity Index                           -0.680598   \n",
              "Life Expectancy                                     0.022329   \n",
              "\n",
              "                               Consumer Opportunity Profile  \\\n",
              "Census Tract                                      -0.272304   \n",
              "Access to Care                                     0.153253   \n",
              "Employment Accessibility                           0.012975   \n",
              "Affordability                                      0.849763   \n",
              "Air Quality                                        0.094587   \n",
              "Population Churning                                0.314793   \n",
              "Education                                          0.850497   \n",
              "Food Accessibility                                 0.501222   \n",
              "Income Inequality                                  0.157814   \n",
              "Job Participation                                  0.179280   \n",
              "Population Density                                 0.082231   \n",
              "Segregation                                        0.125292   \n",
              "Material Deprivation                               0.683564   \n",
              "Walkability                                        0.148133   \n",
              "Community Environment Profile                     -0.071891   \n",
              "Consumer Opportunity Profile                       1.000000   \n",
              "Economic Opportunity Profile                      -0.002735   \n",
              "Wellness Disparity Profile                         0.024372   \n",
              "Health Opportunity Index                           0.474662   \n",
              "Life Expectancy                                    0.667789   \n",
              "\n",
              "                               Economic Opportunity Profile  \\\n",
              "Census Tract                                       0.076913   \n",
              "Access to Care                                     0.149467   \n",
              "Employment Accessibility                           0.742027   \n",
              "Affordability                                      0.268344   \n",
              "Air Quality                                       -0.003314   \n",
              "Population Churning                               -0.009998   \n",
              "Education                                          0.120426   \n",
              "Food Accessibility                                -0.061523   \n",
              "Income Inequality                                  0.787819   \n",
              "Job Participation                                  0.743948   \n",
              "Population Density                                 0.232876   \n",
              "Segregation                                        0.071416   \n",
              "Material Deprivation                               0.143508   \n",
              "Walkability                                        0.185216   \n",
              "Community Environment Profile                      0.076702   \n",
              "Consumer Opportunity Profile                      -0.002735   \n",
              "Economic Opportunity Profile                       1.000000   \n",
              "Wellness Disparity Profile                         0.058921   \n",
              "Health Opportunity Index                           0.438236   \n",
              "Life Expectancy                                    0.122628   \n",
              "\n",
              "                               Wellness Disparity Profile  \\\n",
              "Census Tract                                    -0.054204   \n",
              "Access to Care                                   0.721690   \n",
              "Employment Accessibility                         0.089301   \n",
              "Affordability                                    0.029963   \n",
              "Air Quality                                     -0.174685   \n",
              "Population Churning                             -0.037753   \n",
              "Education                                        0.085361   \n",
              "Food Accessibility                              -0.028813   \n",
              "Income Inequality                               -0.023469   \n",
              "Job Participation                                0.084316   \n",
              "Population Density                               0.038368   \n",
              "Segregation                                     -0.688802   \n",
              "Material Deprivation                            -0.000473   \n",
              "Walkability                                      0.067683   \n",
              "Community Environment Profile                   -0.015893   \n",
              "Consumer Opportunity Profile                     0.024372   \n",
              "Economic Opportunity Profile                     0.058921   \n",
              "Wellness Disparity Profile                       1.000000   \n",
              "Health Opportunity Index                         0.367059   \n",
              "Life Expectancy                                  0.061602   \n",
              "\n",
              "                               Health Opportunity Index  Life Expectancy  \n",
              "Census Tract                                  -0.242221        -0.275855  \n",
              "Access to Care                                 0.258817         0.127604  \n",
              "Employment Accessibility                       0.233853         0.113947  \n",
              "Affordability                                  0.581156         0.646233  \n",
              "Air Quality                                    0.262918         0.015143  \n",
              "Population Churning                            0.586541         0.158760  \n",
              "Education                                      0.343976         0.656399  \n",
              "Food Accessibility                             0.175768         0.237864  \n",
              "Income Inequality                              0.554829         0.185906  \n",
              "Job Participation                              0.230974         0.237451  \n",
              "Population Density                            -0.412225         0.144265  \n",
              "Segregation                                   -0.259205         0.044646  \n",
              "Material Deprivation                           0.760733         0.403099  \n",
              "Walkability                                   -0.381492         0.129092  \n",
              "Community Environment Profile                 -0.680598         0.022329  \n",
              "Consumer Opportunity Profile                   0.474662         0.667789  \n",
              "Economic Opportunity Profile                   0.438236         0.122628  \n",
              "Wellness Disparity Profile                     0.367059         0.061602  \n",
              "Health Opportunity Index                       1.000000         0.344823  \n",
              "Life Expectancy                                0.344823         1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f4deddd3-4bd8-4629-8a8f-ca2c391ba0d2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Census Tract</th>\n",
              "      <th>Access to Care</th>\n",
              "      <th>Employment Accessibility</th>\n",
              "      <th>Affordability</th>\n",
              "      <th>Air Quality</th>\n",
              "      <th>Population Churning</th>\n",
              "      <th>Education</th>\n",
              "      <th>Food Accessibility</th>\n",
              "      <th>Income Inequality</th>\n",
              "      <th>Job Participation</th>\n",
              "      <th>Population Density</th>\n",
              "      <th>Segregation</th>\n",
              "      <th>Material Deprivation</th>\n",
              "      <th>Walkability</th>\n",
              "      <th>Community Environment Profile</th>\n",
              "      <th>Consumer Opportunity Profile</th>\n",
              "      <th>Economic Opportunity Profile</th>\n",
              "      <th>Wellness Disparity Profile</th>\n",
              "      <th>Health Opportunity Index</th>\n",
              "      <th>Life Expectancy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Census Tract</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.101553</td>\n",
              "      <td>0.217050</td>\n",
              "      <td>-0.274574</td>\n",
              "      <td>-0.095797</td>\n",
              "      <td>-0.288376</td>\n",
              "      <td>-0.173096</td>\n",
              "      <td>-0.059004</td>\n",
              "      <td>-0.030739</td>\n",
              "      <td>-0.044878</td>\n",
              "      <td>0.053454</td>\n",
              "      <td>-0.028051</td>\n",
              "      <td>-0.212761</td>\n",
              "      <td>0.286851</td>\n",
              "      <td>0.225648</td>\n",
              "      <td>-0.272304</td>\n",
              "      <td>0.076913</td>\n",
              "      <td>-0.054204</td>\n",
              "      <td>-0.242221</td>\n",
              "      <td>-0.275855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Access to Care</th>\n",
              "      <td>-0.101553</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.174927</td>\n",
              "      <td>0.102286</td>\n",
              "      <td>-0.241885</td>\n",
              "      <td>-0.024942</td>\n",
              "      <td>0.155219</td>\n",
              "      <td>0.047246</td>\n",
              "      <td>0.009472</td>\n",
              "      <td>0.181788</td>\n",
              "      <td>0.099365</td>\n",
              "      <td>0.004721</td>\n",
              "      <td>-0.000611</td>\n",
              "      <td>0.103322</td>\n",
              "      <td>0.133512</td>\n",
              "      <td>0.153253</td>\n",
              "      <td>0.149467</td>\n",
              "      <td>0.721690</td>\n",
              "      <td>0.258817</td>\n",
              "      <td>0.127604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Employment Accessibility</th>\n",
              "      <td>0.217050</td>\n",
              "      <td>0.174927</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.148547</td>\n",
              "      <td>-0.205619</td>\n",
              "      <td>-0.124648</td>\n",
              "      <td>0.205088</td>\n",
              "      <td>0.040581</td>\n",
              "      <td>0.315512</td>\n",
              "      <td>0.578782</td>\n",
              "      <td>0.252585</td>\n",
              "      <td>0.054193</td>\n",
              "      <td>0.021448</td>\n",
              "      <td>0.313642</td>\n",
              "      <td>0.271898</td>\n",
              "      <td>0.012975</td>\n",
              "      <td>0.742027</td>\n",
              "      <td>0.089301</td>\n",
              "      <td>0.233853</td>\n",
              "      <td>0.113947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Affordability</th>\n",
              "      <td>-0.274574</td>\n",
              "      <td>0.102286</td>\n",
              "      <td>0.148547</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.147145</td>\n",
              "      <td>0.301555</td>\n",
              "      <td>0.720131</td>\n",
              "      <td>0.236850</td>\n",
              "      <td>0.371452</td>\n",
              "      <td>0.323145</td>\n",
              "      <td>0.078537</td>\n",
              "      <td>0.063837</td>\n",
              "      <td>0.607648</td>\n",
              "      <td>0.082831</td>\n",
              "      <td>-0.132742</td>\n",
              "      <td>0.849763</td>\n",
              "      <td>0.268344</td>\n",
              "      <td>0.029963</td>\n",
              "      <td>0.581156</td>\n",
              "      <td>0.646233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Air Quality</th>\n",
              "      <td>-0.095797</td>\n",
              "      <td>-0.241885</td>\n",
              "      <td>-0.205619</td>\n",
              "      <td>0.147145</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.237880</td>\n",
              "      <td>-0.001716</td>\n",
              "      <td>0.030507</td>\n",
              "      <td>0.080957</td>\n",
              "      <td>-0.198527</td>\n",
              "      <td>-0.293198</td>\n",
              "      <td>-0.000969</td>\n",
              "      <td>0.299885</td>\n",
              "      <td>-0.310749</td>\n",
              "      <td>-0.524040</td>\n",
              "      <td>0.094587</td>\n",
              "      <td>-0.003314</td>\n",
              "      <td>-0.174685</td>\n",
              "      <td>0.262918</td>\n",
              "      <td>0.015143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Population Churning</th>\n",
              "      <td>-0.288376</td>\n",
              "      <td>-0.024942</td>\n",
              "      <td>-0.124648</td>\n",
              "      <td>0.301555</td>\n",
              "      <td>0.237880</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.056493</td>\n",
              "      <td>0.087803</td>\n",
              "      <td>0.140384</td>\n",
              "      <td>-0.114245</td>\n",
              "      <td>-0.323118</td>\n",
              "      <td>0.028417</td>\n",
              "      <td>0.508192</td>\n",
              "      <td>-0.424650</td>\n",
              "      <td>-0.657965</td>\n",
              "      <td>0.314793</td>\n",
              "      <td>-0.009998</td>\n",
              "      <td>-0.037753</td>\n",
              "      <td>0.586541</td>\n",
              "      <td>0.158760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education</th>\n",
              "      <td>-0.173096</td>\n",
              "      <td>0.155219</td>\n",
              "      <td>0.205088</td>\n",
              "      <td>0.720131</td>\n",
              "      <td>-0.001716</td>\n",
              "      <td>0.056493</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.222487</td>\n",
              "      <td>0.145495</td>\n",
              "      <td>0.283563</td>\n",
              "      <td>0.200327</td>\n",
              "      <td>0.039245</td>\n",
              "      <td>0.494881</td>\n",
              "      <td>0.315842</td>\n",
              "      <td>0.151846</td>\n",
              "      <td>0.850497</td>\n",
              "      <td>0.120426</td>\n",
              "      <td>0.085361</td>\n",
              "      <td>0.343976</td>\n",
              "      <td>0.656399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Food Accessibility</th>\n",
              "      <td>-0.059004</td>\n",
              "      <td>0.047246</td>\n",
              "      <td>0.040581</td>\n",
              "      <td>0.236850</td>\n",
              "      <td>0.030507</td>\n",
              "      <td>0.087803</td>\n",
              "      <td>0.222487</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.117935</td>\n",
              "      <td>0.110582</td>\n",
              "      <td>0.002299</td>\n",
              "      <td>0.091104</td>\n",
              "      <td>0.196838</td>\n",
              "      <td>0.063371</td>\n",
              "      <td>-0.009147</td>\n",
              "      <td>0.501222</td>\n",
              "      <td>-0.061523</td>\n",
              "      <td>-0.028813</td>\n",
              "      <td>0.175768</td>\n",
              "      <td>0.237864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income Inequality</th>\n",
              "      <td>-0.030739</td>\n",
              "      <td>0.009472</td>\n",
              "      <td>0.315512</td>\n",
              "      <td>0.371452</td>\n",
              "      <td>0.080957</td>\n",
              "      <td>0.140384</td>\n",
              "      <td>0.145495</td>\n",
              "      <td>0.117935</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.409413</td>\n",
              "      <td>0.030091</td>\n",
              "      <td>0.043823</td>\n",
              "      <td>0.352046</td>\n",
              "      <td>-0.028158</td>\n",
              "      <td>-0.201656</td>\n",
              "      <td>0.157814</td>\n",
              "      <td>0.787819</td>\n",
              "      <td>-0.023469</td>\n",
              "      <td>0.554829</td>\n",
              "      <td>0.185906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Job Participation</th>\n",
              "      <td>-0.044878</td>\n",
              "      <td>0.181788</td>\n",
              "      <td>0.578782</td>\n",
              "      <td>0.323145</td>\n",
              "      <td>-0.198527</td>\n",
              "      <td>-0.114245</td>\n",
              "      <td>0.283563</td>\n",
              "      <td>0.110582</td>\n",
              "      <td>0.409413</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.386398</td>\n",
              "      <td>0.068581</td>\n",
              "      <td>-0.013987</td>\n",
              "      <td>0.353069</td>\n",
              "      <td>0.354718</td>\n",
              "      <td>0.179280</td>\n",
              "      <td>0.743948</td>\n",
              "      <td>0.084316</td>\n",
              "      <td>0.230974</td>\n",
              "      <td>0.237451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Population Density</th>\n",
              "      <td>0.053454</td>\n",
              "      <td>0.099365</td>\n",
              "      <td>0.252585</td>\n",
              "      <td>0.078537</td>\n",
              "      <td>-0.293198</td>\n",
              "      <td>-0.323118</td>\n",
              "      <td>0.200327</td>\n",
              "      <td>0.002299</td>\n",
              "      <td>0.030091</td>\n",
              "      <td>0.386398</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.048638</td>\n",
              "      <td>-0.332270</td>\n",
              "      <td>0.630983</td>\n",
              "      <td>0.796854</td>\n",
              "      <td>0.082231</td>\n",
              "      <td>0.232876</td>\n",
              "      <td>0.038368</td>\n",
              "      <td>-0.412225</td>\n",
              "      <td>0.144265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Segregation</th>\n",
              "      <td>-0.028051</td>\n",
              "      <td>0.004721</td>\n",
              "      <td>0.054193</td>\n",
              "      <td>0.063837</td>\n",
              "      <td>-0.000969</td>\n",
              "      <td>0.028417</td>\n",
              "      <td>0.039245</td>\n",
              "      <td>0.091104</td>\n",
              "      <td>0.043823</td>\n",
              "      <td>0.068581</td>\n",
              "      <td>0.048638</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>0.010431</td>\n",
              "      <td>0.162785</td>\n",
              "      <td>0.125292</td>\n",
              "      <td>0.071416</td>\n",
              "      <td>-0.688802</td>\n",
              "      <td>-0.259205</td>\n",
              "      <td>0.044646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Material Deprivation</th>\n",
              "      <td>-0.212761</td>\n",
              "      <td>-0.000611</td>\n",
              "      <td>0.021448</td>\n",
              "      <td>0.607648</td>\n",
              "      <td>0.299885</td>\n",
              "      <td>0.508192</td>\n",
              "      <td>0.494881</td>\n",
              "      <td>0.196838</td>\n",
              "      <td>0.352046</td>\n",
              "      <td>-0.013987</td>\n",
              "      <td>-0.332270</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.282516</td>\n",
              "      <td>-0.590112</td>\n",
              "      <td>0.683564</td>\n",
              "      <td>0.143508</td>\n",
              "      <td>-0.000473</td>\n",
              "      <td>0.760733</td>\n",
              "      <td>0.403099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Walkability</th>\n",
              "      <td>0.286851</td>\n",
              "      <td>0.103322</td>\n",
              "      <td>0.313642</td>\n",
              "      <td>0.082831</td>\n",
              "      <td>-0.310749</td>\n",
              "      <td>-0.424650</td>\n",
              "      <td>0.315842</td>\n",
              "      <td>0.063371</td>\n",
              "      <td>-0.028158</td>\n",
              "      <td>0.353069</td>\n",
              "      <td>0.630983</td>\n",
              "      <td>0.010431</td>\n",
              "      <td>-0.282516</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.812265</td>\n",
              "      <td>0.148133</td>\n",
              "      <td>0.185216</td>\n",
              "      <td>0.067683</td>\n",
              "      <td>-0.381492</td>\n",
              "      <td>0.129092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Community Environment Profile</th>\n",
              "      <td>0.225648</td>\n",
              "      <td>0.133512</td>\n",
              "      <td>0.271898</td>\n",
              "      <td>-0.132742</td>\n",
              "      <td>-0.524040</td>\n",
              "      <td>-0.657965</td>\n",
              "      <td>0.151846</td>\n",
              "      <td>-0.009147</td>\n",
              "      <td>-0.201656</td>\n",
              "      <td>0.354718</td>\n",
              "      <td>0.796854</td>\n",
              "      <td>0.162785</td>\n",
              "      <td>-0.590112</td>\n",
              "      <td>0.812265</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.071891</td>\n",
              "      <td>0.076702</td>\n",
              "      <td>-0.015893</td>\n",
              "      <td>-0.680598</td>\n",
              "      <td>0.022329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer Opportunity Profile</th>\n",
              "      <td>-0.272304</td>\n",
              "      <td>0.153253</td>\n",
              "      <td>0.012975</td>\n",
              "      <td>0.849763</td>\n",
              "      <td>0.094587</td>\n",
              "      <td>0.314793</td>\n",
              "      <td>0.850497</td>\n",
              "      <td>0.501222</td>\n",
              "      <td>0.157814</td>\n",
              "      <td>0.179280</td>\n",
              "      <td>0.082231</td>\n",
              "      <td>0.125292</td>\n",
              "      <td>0.683564</td>\n",
              "      <td>0.148133</td>\n",
              "      <td>-0.071891</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.002735</td>\n",
              "      <td>0.024372</td>\n",
              "      <td>0.474662</td>\n",
              "      <td>0.667789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Economic Opportunity Profile</th>\n",
              "      <td>0.076913</td>\n",
              "      <td>0.149467</td>\n",
              "      <td>0.742027</td>\n",
              "      <td>0.268344</td>\n",
              "      <td>-0.003314</td>\n",
              "      <td>-0.009998</td>\n",
              "      <td>0.120426</td>\n",
              "      <td>-0.061523</td>\n",
              "      <td>0.787819</td>\n",
              "      <td>0.743948</td>\n",
              "      <td>0.232876</td>\n",
              "      <td>0.071416</td>\n",
              "      <td>0.143508</td>\n",
              "      <td>0.185216</td>\n",
              "      <td>0.076702</td>\n",
              "      <td>-0.002735</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.058921</td>\n",
              "      <td>0.438236</td>\n",
              "      <td>0.122628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wellness Disparity Profile</th>\n",
              "      <td>-0.054204</td>\n",
              "      <td>0.721690</td>\n",
              "      <td>0.089301</td>\n",
              "      <td>0.029963</td>\n",
              "      <td>-0.174685</td>\n",
              "      <td>-0.037753</td>\n",
              "      <td>0.085361</td>\n",
              "      <td>-0.028813</td>\n",
              "      <td>-0.023469</td>\n",
              "      <td>0.084316</td>\n",
              "      <td>0.038368</td>\n",
              "      <td>-0.688802</td>\n",
              "      <td>-0.000473</td>\n",
              "      <td>0.067683</td>\n",
              "      <td>-0.015893</td>\n",
              "      <td>0.024372</td>\n",
              "      <td>0.058921</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.367059</td>\n",
              "      <td>0.061602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Health Opportunity Index</th>\n",
              "      <td>-0.242221</td>\n",
              "      <td>0.258817</td>\n",
              "      <td>0.233853</td>\n",
              "      <td>0.581156</td>\n",
              "      <td>0.262918</td>\n",
              "      <td>0.586541</td>\n",
              "      <td>0.343976</td>\n",
              "      <td>0.175768</td>\n",
              "      <td>0.554829</td>\n",
              "      <td>0.230974</td>\n",
              "      <td>-0.412225</td>\n",
              "      <td>-0.259205</td>\n",
              "      <td>0.760733</td>\n",
              "      <td>-0.381492</td>\n",
              "      <td>-0.680598</td>\n",
              "      <td>0.474662</td>\n",
              "      <td>0.438236</td>\n",
              "      <td>0.367059</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.344823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Life Expectancy</th>\n",
              "      <td>-0.275855</td>\n",
              "      <td>0.127604</td>\n",
              "      <td>0.113947</td>\n",
              "      <td>0.646233</td>\n",
              "      <td>0.015143</td>\n",
              "      <td>0.158760</td>\n",
              "      <td>0.656399</td>\n",
              "      <td>0.237864</td>\n",
              "      <td>0.185906</td>\n",
              "      <td>0.237451</td>\n",
              "      <td>0.144265</td>\n",
              "      <td>0.044646</td>\n",
              "      <td>0.403099</td>\n",
              "      <td>0.129092</td>\n",
              "      <td>0.022329</td>\n",
              "      <td>0.667789</td>\n",
              "      <td>0.122628</td>\n",
              "      <td>0.061602</td>\n",
              "      <td>0.344823</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4deddd3-4bd8-4629-8a8f-ca2c391ba0d2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f4deddd3-4bd8-4629-8a8f-ca2c391ba0d2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f4deddd3-4bd8-4629-8a8f-ca2c391ba0d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fullData.plot(x=\"Affordability\", y=\"Life Expectancy\", style=\"o\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "u8Yp8uEFgWcM",
        "outputId": "cc77a2a2-c1ac-4ef5-cb2e-932f1137a425"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4176644d00>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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UwRYq5MLAaktzhr0esYHW/H5+qaS9b/V7xiBqgajB2FFAk4hkgWZgV8T95gMPq+oeABF5GDiTgjmzhmGUn2JL/X0tYIVMo5AdOKTt3XcFYUo6SvOwoPz9KMf0CqxmGiRQbucaLVm3dYjbaW9vtmaDslGGg3cDy4EdwCvA66r6UP7lG0XkaRG5WUQO89i9FXjZ9Xhn/rlhiMgVIrJBRDbs3l1fTf8NI0nK3UWykCCXxdjRo3zdLgvnT/UNejaKRGpD0NneOujaCcM5PzB4fa6+a/OwRSJ7UAPlds479rDhdnCtBmWjuG7GA+cAU4Ae4G4RuQS4BvgvYDSwEvh74PpiBVHVlfnj0NHRUWzxm2HUNdVI/Vs4fypXrdrk+drrfVk2LT7D87XO9lbf/Q6qRs5XH1w47t48rK88wCWnTBpMqSy8Pn5xBD+53fJE7b1TC0QJxp4GbFfV3aqaBdYA71PVVzTHAeD7wEke+3YDx7oeH5N/zjCMIqhG6l9neyvjQ9Ig/fCzxI9uafIM8vqlRy578HlPJQ+wemP34D5R++B4yV0oT5x9S6Xcd2lRFP0O4BQRaRYRAeYBz4nIOwDyz3UCz3js+yBwhoiMz98ZnJF/zjCMIig29a9URbL4I9MD0yD9jr9w/lQyDUMdOJkGYeH8qbEWraD3594nirXtFxuIskiUYyhJnAWvWEJdN6r6uIjcAzwF9ANd5FwsD4jIRHLpp5uAKwFEpAO4UlUvV9U9IvJl4Mn84a53ArOGYcTHL8DpZWVeu3YLdzz+8jD3RTHunqABHUHuJIBhjnqBDS/t8Q3Udvf0MWfpI0POExbYdRS833aNIhxUDcwuCloknBYL5WiFkHSvfy9E41YkVICOjg7dsGFDtcUwjNRRqFQhZ2UWBhOvXbtlSC8bL5Jq2et0lvQ6Pni3G4jThbIp08h5s1uHNDfzOteji06NfH3ivI8oi0Qp+PUOEmD70g9FPo6IbFTVDq/XrNeNYcSg2s2uoo6+u+Pxl712H0JSQcVi3ElxzMu+7AA/fmxHPk6g9GUPDnndcae4e9U7zdK8ulb6XTu/VE7njqhcge84d2nFYoreMCKSlmZXUfLh/bJN3CSlSMIUValj/xz29mZpyjRyySmTuP/pVwaLng4b1cCGl/YMsfgHVAcXgKitiQsX0QaPzppJu1TAe4FJOhZgvW4MIyLlyHgpV7aFVx8aN0kqEr/2Cr1v9TN32sRhrwVLFkxfdoCfbn6F/S6rvqcvy22P7Qj9bKJ8fp3trTy66FS2L/0QB0NaPCSFu1bAL6+/VMyiN4yIFOOiCHIVlPMO4aKTj/X10UctVopKZ3srG17aw22P7xjSEmFvb5bVG7s5b3brkFYLc6dN5LbHdhQ9KcqrSVqUnPe4n18cl0qpLr1yDyg3i94wIhK32VVY2lw5c+Jv6JzBnHdOGPZ8U6aRr35sVqJKZW1XN6s3dnv2venLDrB+2+5BK/nRRadyQ+eMopV8XJyZtmu7umN/fl53Kl53QpVIjywVU/SGEZGoP3yHMEVe7na4t336L1hxQVtZXQIQnn/enR/m4SZKS4Ok6O7pY8GqTYMzZ90EfX5RXSq10L/eXDeGEZGoGS8OYYq8EtkW5XQJOO6KKMHWQpdU3GZlYTQ2CAM+lbNwyLXjnlVbmJHjRZTrVwv9603RG0YM4ijOMEUelm1RiVTOOOdwb9vSnOHN/f2+bQkK6csOcPVdm1mwatPgec6b3epZ0FUMQUq+EPdM2iSoxIJdKua6MYwyEebqCXINVMLvG+cchdvu7c1GVvIOA6qD51l492ZWPTlUyYcNRkmSsLuQONlQcV161cAsesMoE1FcPX53CMWWxcex0OOcI2qzsKh4LRLuQqdyI+A7RCRuNlRcl141MEVvGGUk6WEfYamccRRUnHNE9Te3FjERys2A6rChIOVAwXfRLGaRLXd6ZKmY68YwUkgxc0vjZn/4HatBZJjLIqq/ee60iSVl1LS2NLHs/Fm+bZGTJO5iGnWxq/RgmCiYojeMFFKM3zeugvKraHX70h2ffZTh4gCrnnzZsxo2Ko7Lo+sfz+DFpR9ixQVtiVbWuom7mLZEWHzSmlNvit4wUkgxZfFxFVfhObzaJrhdFlFG+mUHlPuffoXzZreGtmHwYsm6rUMs4c721iHHahThfe+cMEz5ZxrjnSto0Vw4f6rn8d7c3x+qsNOaU29tig0jBsWkPBamJarmRtklHbQrpUUvRG+X23bdQ55tCNw0ZRpLDt76tSd2nne3Vdh3oD9UJodGkdDqYL/3GJaWmVTL4WIouU2xiCwALicXw9gCfAr4HtABZIEngM/kRw0W7juQ3wdgh6qeHfsdGEYKKKY3TeE+TsfFqPvHodjsD2dAiZ/JV3hH8HoEhZpEhk5fdsAzz97dVsFhyqL7Ix93ID+v1o+1Xd2+i4bXUBT3sdKaUx9lOHgr8DngBFXtE5G7gAuB24BL8pvdTm4h+JbHIfpUtS0heQ2jahSTjRGWlph029u42R9hA0q8XBxh056SxC/Vsrunj8l55T6+OUNLc2bIIhpEkEvJWZj9EA7l4Hst1JVoOVwMUdMrRwFNIpIFmoFdqvqQ86KIPEFu8Ldh1C3FZGNEydSoZKl8oesp6Nx+LQLC2hc0CMSspSqJvb1ZGoTIaZlBefpBC7PXVKzChTqtOfVRZsZ2i8hyckPC+4CHCpR8BvgfwOd9DjFGRDaQmze7VFXXem0kIlcAVwBMmjQp1pswjEpQzG15FOs37m19sa0R1nZ1s/CezYPKMEwuxzXidb6bzp3BknVbPV0clVTy7nP+2ehRjD1s1KCcvW/1e1r5rS1NvtewmKlYhfukMac+NOtGRMYD5wBTgKOBsSJyiWuTbwK/UtX/8DnEcfkAwceBFSLyTq+NVHWlqnaoasfEiRNjvQnDqATFpDyGpSXGva0vJX3vuvu2Ri5Ectwbfue7e8OOSL76StLTlx3SDnnxR6Z7fl5zp030vYZ+i25rS5NvxlG1/e9RiJJeeRqwXVV354Ota4D3AYjIYmAi8AW/nVW1O///H4BfAu0lymwYVaGYlMfCfcY3Z2hpyhTdNriU9L2oPmzIDS4JOt+jL+xJrKd8MWmYXogwpFAJ8Py81m/b7XsNgxbzWuhp40cUH/0O4BQRaSbnupkHbBCRy4H5wDxVPei1Y/5uoFdVD4jIUcAc4J+TEd0wKk8xt+VJ3sqHxQmKdes4PWYaRbjo5GO5oXNG4PmSJKneNqpDA6UL797Mso/OGpYOuWDVJs/9d/X0RfKxp83/HoUoPvrHReQe4ClyfvYuYCWwD3gJ+E/JrchrVPV6EekArlTVy4HjgVtE5CC5u4elqvpsed6KYdQ+YYo6KE4Qlv7pFUyEnCX8wk1nDZFhztJHKpZZU65GZtmDypJ1W4cp4rBYS9DC7PVaJdpJl4oVTBlGSohS8BS0TdAQkJamTGBB0Yv5Yh6v4yeFAKMKMmP8iqKS5MWCQqVSC8vKdaxSCSqYshYIhpESovjfC1sRNIoMbhNkgQcpeXeQMel2xG6UXIsEdzuDvuwA9z/9Cv0D5TmnG+dOZcGqTYzJNJQUK3FIa8uDQqxNsWGkhKh5+o5CKnTTFIs7mBjHJ1+sy8XZx/k/TpC4GNque4gPz3rHkLuGvb1ZmjKN3HxB25C7pbgumFoYIwhm0RtGaghqG1yYPpmU5V2Y7xInVbASA0KSoKcvy22P7fC0vK+7bytQfNpqMe2kq4EpesMoI6WOpIOcQi1UOklZjApDjl0LqYLF4Lck7e3NDlryxbhgaiXl0hS9YZSJuFai438PahfskKTFmEafclTGN2fINJSWh79k3daiXTDF1FZUA1P0hlEmirESO9tbOejjEnErnaiDQKLiHLtWFH5TppEVF7TR9Y9nsOyjswYDysWo/J58y2gvoiyone2tQypy06bkwYKxhlEycfumuJ/32jdKTx13YU8S+e7OsZNwCfnl6ydFS1OGJWdPH9JIzB1Qve6+rbEDvHOnTfTse582F0yxWB69YcTErZzHNWXY91b/sNzwoLx2Z3iFXw521GEb7qyQYhWcw/jmDIs/Mj2xhaNSOHLD0IrVudMm8tPNr0QeRuJ06kx74VMQQXn0pugNIwZRC4ocxRFUTONXfeqldPwsTi9/8Nqubq7yKfMPwllMVj3xMtlqtKBMkEyj0D+gke8sKjEBqtyUPGHKMIwcUdMao/RNCXLtFJbaz1n6iG96oLtdcHOmgf39nq2nQunLDvDTza8kN327ikTt0umQtnTIpDFFb9QNleg5EtWHHaVvSpz+9n7nLXTV9GaLU/IOUV0d9UQ9+eL9MEVv1AXFzHMthiiDRKIqDj/XztxpE4fNJa3k+L6RxmGj/JMPHeOhu6dvsBLYb/JWmjEfvTGMWujGV0hQt8Ukf5hePvpMg3D4mFH09GZjX6/Ca+3ni3/PpHH8JoEe8OXOiEkLUccKOghw8SmTBtszQ3A8xmv7amM+eiMylbKMi6WYEXBJvoekZ4JG9cUXKnkBxmQa6IvpqhkJSr5B4vvoFQaHpDvKOygeo8Btj+2g47gJqfhdhGGK3hhCUJFPZ3trVa39oEUozLVROMS5FMo5E9RvwSpUW0pO2WcapOYzZJKk1MHkbmUfFo9RSOw7VW4iVcaKyAIR2Soiz4jIHSIyRkSmiMjjIvJ7EVklIqN99r0mv83zIjI/WfGNpAnKBCllXmkSBC1CUSpF/d5bnH405SZO9kdv9iAnTRlfRmlqjyTWvNse2xE4P9ZN2rpU+hFlOHgr8DmgQ1VPBBqBC4F/Am5W1f8G7AX+2mPfE/LbTgfOBL4pIsnVbRuJE1QKXu3e22HpiO4+7V54vbdKLV5RF5O50ybGOu6jL+xJQjzDhZIbpB7FeKiVtMyovW5GAU0iMgpoBl4BTgXuyb/+Q6DTY79zgDtV9YCqbgd+D5xUmshGOQnqxlft3tth/UicniMrLmiL3FGw3IvX2q5u2q57iKtWbYq0mKzftjuR8xrBBGXawKG01SDjoZbSMkMVvap2A8vJDQl/BXgd2Aj0qGp/frOdgJejqhV42fXYbztE5AoR2SAiG3bvti97tQjqxlft3ttRW8LG6ShYzsXLuVvwyk33W0wshbIyHHX4YVxyyqTAbRz/+6OLTuXFpR9ixQVtqe9S6UdoMFZExpOzzKcAPcDd5NwwiaKqK8kNHaejo8OiS1XEL9jol/ddqlUTNcAbJ+MlasA0TtFSXMKqaGvFv1uP7OrpG8yucQKwXtu4KWcQvtxEybo5DdiuqrsBRGQNMAdoEZFReav+GMDL6dgNHOt67LedUQMknVoI8dM5k/6xlWvxgnBFXiv+3XrEufY3dM7wbX5WT59PFEW/AzhFRJqBPmAesAFYD5wP3AlcCvzEY991wO0i8jXgaOBdwBMJyG1UiaQVbVg6Z7kpZvGKegcSlPIpxA+8GskgDJ2kteTs6WVb7NNCqKJX1cdF5B7gKaAf6CLnYrkfuFNEbsg/9z0AETmbXIbOP6rqVhG5C3g2v+/fqmr5x70bNUO1A7wQb/GKcwfidbfgoMDqjd3DCm6aIhRBzXnnBLbu+lPsvjQjpSoW/AeXOxWt7mtejjvVtGEtEIyqEtSq99FFp1ZBomDiyuvuleJF4X5BLYYbRbjo5GO5oXNGYMsHP1pHUL8cp+1wLbbzKBZrgWCklnL6yN0k9YOPewfi3C1MWXS/pzXttV9htWumQVj20VlAzur0O1YY+w70x+4BU6tE6R46kjBFb1SVStw2J9m/JyhLJ2gxiZrds+zB54e1NMgeVJas28qB/oOReuH70dOXJdMgjG/O0NObpaU5U/REqjRTb/71JLDh4CkgTSX41aDcw5WTLIryq5bcs+8AC+/Z7FsU5Rd4dT+/tqvb17XS05ctSck7ZA8qzaNHcfMFbewvsXd9OZjzzglD8tXHN2doacoM+RuGz0ZxHo9vznDYqAYWrNo0In9LfphFX2XS3i2yHkgy4Ot8JoXzWb0CqH3ZAa6+azPgX/HqPO98DypBd08fV9+12TNYWW0e+8Nebvv0X4R+973unoDEf0v14uM3RV9lqp1eOBIIc5vE/TF3trey7MHnI7k9BlRZsGqTr+cLWEYAACAASURBVE/dWWyCiquaMo2MyTQk6maJo+QbBSrl1o8ql5fv3a/Fc7G/pXoywsx1U2XSkF6YBsrpvgpqneDV1GzBqk1cuzbYuo7z+QSpLmexCTreTefOYPFHpoc22CoXlYzdNkrwwNqg70nSv6VqN/FLElP0Vaba/WPSQLk7SHr1vjlvds4qv2rVpmE/ZmeoRND5i/l8ClWYO2jod7zWlqZB63FMpv5/rhedfKzva2Hfk6R/S/VkhNX/NyflRG3UVc9UwnJyB3wXzp/K6o3+gU84NFTCi7Vd3fS+1T/s+UxDsDWq4NsUyy9Yu3ffAa5du4Vr1mwJdN00inDJKZOqZvW7edfbxhJyKTxpbBA6jpvg+3rY9yTp31I9GWHmo68yI6EqL4xKW05hzcaCzu83R7SlKcOHZ72DVU++7Jun3tKU8S0C8wvW9mYP+jbdcmjKNA4uGh3HTeALd21KZABHsfzuj/uK2m/goAb608O+J0n/lipV41EJTNGngJFe1OEXLB2XT6VLmqgLiJfl5rdIjD1sFOu37Q4sRgpyPxe7qI1vzrA/O8BVqzZx1apNjB3dWNN9DoKuQ5RahCR/S/VkhJmiN6rOwvlTWXj35mGFQvve6mdtV/ewH1acLBmvbcPmy4K/5ea3X3dP3zAffCE9Aa6XKDJ5UejO2fdWbbeSCnKLVMPCrhcjzHz0RtXpbG/l8DHDbY7sgHLdfVuHPBcncOu37dxpE4f5cjONMliY4w7WFmZ3+GWFNIqE+m7DlFil/OsrLmjLWf4BlDPu29KU8YwnhCntOANljKGYRW+kAj9rd29vdohVH6fuwG/b9dt2c9O5M3zvCoLyp/3yvAdUA7tVFioxrzuN82a3hvrjk8CvaZqbchXNCrm2wM5n43SZbPX4DLw+n3qxsCuNKXojFQS5LtxKPE7gNmyYuJ/C8Fsgrrtvq2+rX3capNOt0k+JXbt2yxCF3t3TF0n51gPjmjJDFsMB1cFFMMpCa0q+OMx1Y6SCoFt2t8KOk/JWbHqc3wKxtzfrqeTdgyycNE73PFJHkbdf/xDHf+mBiljtaaQp04gIvouoQz0VKqWFUEUvIlNFZJPr3xsicpWIrHI996KIeJok+de25LezJvOGJ53trYMNqwpxK+Y4udLF5lXHzZNWcvI7VZuTF93Pjx/bMczNs7c3GzpUpJ656dwZgS66yfl4iN+dXS0WKqWFUEWvqs+rapuqtgGzgV7gXlW9wPX8amBNwGHm5rf1bIpvGJDz3YYpZndADnJBUMfaKwzIFhu881sg/Bai1nyLYifwawzHcW2FLaJB2Uu1WKiUFuL66OcBL6jqS84TIiLAx4D0jQMyaoqoecvO4zA/blAaZtBrfnIUnhMOLURRi7BGKk5foX0HhlcUF+LlHqvVQqW0EGuUoIjcCjylqv/meu79wNf8rHUR2Q7sJff53aKqK8POY6MEjTDCRvp5VbA6FaTgrbCjWPt+C0SxU5/KxfjmDB+a+Q5+uvmV2LNlyyXPm/v7h9VKRKE508BXzp1pgdgQEhklKCKjgbOBawpeugi4I2DXv1TVbhF5G/CwiGxT1V95HP8K4AqASZMmFb5sGEMIy74JC+hFSdEMSvErpNiCp3IwvjnD4o9Mp7O9lRs6Zww+P3nR/VWTqZQWywf61ZR8icTJuvkgOWv+VecJERkFnAus8ttJVbvz//8RuBc4yWe7laraoaodEyd6N3gyDIewjJqghSCoutXBr33xZJ82ygvnTw1talYsrS1NzHnnhNDKW4e9vVnPIrLWlPq4w95XGgek1BpxFL2X5X4asE1Vd3rtICJjReQI52/gDOCZYgQ1apuk+82HZdQELQRBPc8d+a67b6tn+2Lwrsb1q+4tlZamDJOPbOI3L+yJ5RoqTEf067iZJE2ZRi45ZVKsBaW1pYmbL2jzDXRDeI96I5xI38y8kj4d+EzBSxdSoPxF5Gjgu6p6FvB24N5cvJZRwO2q+u+lCm2km0KXx9xpE1m9sbvoApigwKnf80F9UYKKkxzrPQwvV09QL5ti6enL8ugLe4rat7unbzCW4VfolSTnzc51z/TrxFlIpkEGF+YD/f5pp0E96o1oxArGVgoLxtYuXkHQoGpSv7a9QccrNXAalKsdBwG2L/3Q4OOkjlurZBoFlMgB10yjsOz8WYOVxF5ccsqkIXEGw5+gYKxVxhqJ4hUE9fvZOxZnkDunlCrJzvbWwW6Vu3r6BnPtk2ogVuge8hseMlLIDmisrJrsgA4uxF4ImJJPCOt1YyRKnOpF4ZCbxM+dU8pQEr+eKTedO4PzZrdyx+MvlxTom3xkE3OWPjJ4xxAlR9wYinPtwvrMG6VhFr2RKFF/nF7uHC9LvZRxbn53A9eseZrbPFoUxOU3L+wZkpVTbL76+ObyDFhJC0GhVMelVo1xmuUcSJ82TNEbiRLFLTK+OePrzim01OdOmxg4VDsIP6u/L3swkcBkEsdobWmieXT4jXWZMjeHETeVM4ymTCMXnzLJM6vG3bWy0n3myz2QPm2Y68ZIlMJWvV40jx5F8+hRobfra7u6Wb2xe4hCFXLZHVGUQJqKmLxwFN2CCC2Kyz0DtjDA7QSy416/lqYMYw8bNSwAfkPnjNC2E5Usiooz16AeMEVvJI7zo/VrC9Dd08clp0waknIJwy11v8Bu1PS9oEEgpZJEuqKjWItRqEkh4NlTyPkM42QSNWUaWXL2dF9FmaahIZUeSF9tTNEbZSPIol69sZvzZreyfttu3wZmpf4YvXLte9/qL6kcH3KuhcLagLi0NGUCc/6dbcrZp8YrvdVtyTuDU6Ieq5YGZ4+0ALApeqNsBFnUzki/oDz6JH6MhVakV15+VAS4+YK2weN1HDehqMlQmQZhydnTh8gIhxakluYMqvB6X7ZshU7uYiWHwmsTRcnXap57NQaNVxNT9EbRBPlc4ZAC81OGYZZ5OX6Mne2tbHhpT1GplQosWLWJZQ8+P/he47pdvMYKOrI0ivC+d07gqR2vl7/lsSvaurarmyXrtsa+e/BS8mHfibQQtSV2vWCK3iiKqHM9g5RhmGUe58cYVcE4Ad5iUyvdGRqQywq67bEdka3u/3p9Pxte2kNne+uw2bEDqkW3O4iLU6wEsPDuzbHbB49vztBx3IQhdQSltrqIQxILSppiBuXGWiAYRRHWD95NKW0MohDn+Em3KSjWtXLJKZNKLtgqFSE3rDuuJd+UaeS82a3DYhTFtrqIq7TL/X2qVawFgpE4cQKl5c6T9kuVcw+cDpO7WIpV09VW8gAtzdGVvOPpcT679dt2R251EXTNi8lnt+Hh8THXjVEUfoHSFp8qz3LeJvspkr29WdZ2dQ85bzG59eUIiCap5JszDSgSy6/flGkkqghOXAFySnbBqk2xrkeQi66YfPaRlhqZBGbRG0WxcP7UXLfCAt7c31/x6sIgRbJk3dYhZe5zp02M3dCseXRjIk3QyoEAo0c10pcdiNW3/bBRDaHW/IoL2nhx6YcG3S5uyztIHjdhwfNilHYpbTFGKqbojaLobG9lrEfpfvagVvwWOkiR9PRlh7gFnPz9OMMx9r01wJhMOn8qCoMKO85dQpiSd+f5g7flXYjT7iCOi64YpV2t3ji1jLlujKJ53UdZVPoWurO9NXJ6oDt/P05gttQiq1qiMM8fgj9Tv+raKBSTQjvSUiOTIFTRi8hUhs6E/XPgH4EW4NOAU4/+v1T1Zx77nwn8C9BIbvLU0lKFNtJBmqoLl5w9PXIhlKO0ytkiwQsnM6SaLQ+8GDu6kX1v5a5BS1NmUMm7UydbmjOei12U4TFBFKu0R1JqZBKEKnpVfR5oAxCRRqCb3JDvTwE3q+pyv33z23+D3BjCncCTIrJOVZ9NQHajyqSpujBOuwNnIfLaZ+60iazftruoZl4H+g8OWzSaMw30Zg/SKLlg6ZJ1W3mrf/jCkmkQMo1Cb9Z/pF4hSQWJW5pHs/X6Q8raq0bCkS87cOiMSX3WprTLT1zXzTzgBVV9SaIFfk4Cfq+qfwAQkTuBcwBT9HVA2m6h3QrDqfYspFA5Fe7jvJc4StTt6ii8FsAQpenlXhrfnGHxR6YPFlEVFmA5dwKFx/fqt9MARF8qchS6Zbz88dmD6tuZ0kg/cRV94TDwvxORTwAbgKtVdW/B9q3Ay67HO4GTvQ4sIlcAVwBMmjQppljRqZUS7VohjdaYXz8bt0KNuk8hY0c3MnpUw+CdguPqcLfbdR/z6rs2hwZJnX70jqvE3evGq52vm47jJgx+n8c1Zdj3Vj8HB+LZ+YWuNj9//Ot9WTYtPiPWsY10EFnRi8ho4GzgmvxT3wK+TM7w+TLwVeCyYgVR1ZXASshVxhZ7nCCilu0b6aDYRdkvQ6R59Cjf/aNklUAuA2fr9WdGkv2aNVsiZcI430Pn/Ht7s4MZLOu37R7WX8d9DreSf2N/NrBv/djRjfS+NTDsbqHQ/ZKm2IuRDHEs+g8CT6nqqwDO/wAi8h3gpx77dAPHuh4fk3+uKoy0YQO1TJRF2W8hKCY3O2qmUIMMDVIWKl93k7KoOP57N33ZgSEunML3v7are0iPmrCMo6ZMIzf+1XD3j9fimWTsxe6g00Gc5OCLcLltROQdrtf+CnjGY58ngXeJyJT8HcGFwLpiBE0Cq6hLH35zO8PK3INK54vJzY5qrR5UfMv1nSZlcate/bYPmqm7ZN3WyI3IGkWG5LPvO9A/KP91920dVuCWVMuKkTauL81EsuhFZCy5zJnPuJ7+ZxFpI/d9fNF5TUSOJpdGeZaq9ovI3wEPkkuvvFVVh0fIKoTdkqaLIKs9bFEOWgiKsUiLTbV03xHe8fjL4TuUiPP+o/aocTf7KrwLgJybaOE9m4HhXUdLtbztDjo9RFL0qroPOLLguf/hs+0u4CzX458Bw/Lrq0Ga0gGNYEUQtiiHLQSHjWoYPHZQENahMIPICWxmIwQ2HTmT7F/jl/UT1yhx3wUse/B5z7uA7IBy9V2HlH1S7ha7g04PI6oyNm3pgLVOqQohSBHcfEFb4KIc1FStcL/9EXPTvaZRuac++VXHOj1mgkbvNWUa6D+okRYOv1GF7vc/PkCeQgqDvV4MqHLNmi1seGlPYj3l7Q46PaSzgUcZ6Wxv5dFFp7I936xppCp5P994nP1L9b8G+dLD/MRzp0303Hd/diCxFrbOd+XmC9oCFwtHuV908rG+24BwwXuPDe2xI8Cji07lhs4Zge9/8UemezaV8yNK07O+7AB3PP5yYtfPetKkhxFl0Rs5kkgzTcL/GuZKC/ITr9+22/P5Ph+FXIq7ICz10lHezlg999SoQ3Id6rGztqvbt9Wve/ELev+Fd6cNEQZ5D6iSaZDAIK7fMYq5fnYHnR7qRtFbGlc0/Ip44irpJPyvpSiCuIqnGHeB850Ka4fgvru4oXOG72jB7p6+wf74G17a41kB62XtFubLi0BP79BiqigFXyJw+JhRgS4fP/dTse6WNBbUjUTqQtFbIVQ0wop44ijPpPyvxSoCv/OPb86wP3uw5IB71EpZyFnw67ftHlS6QcNNnO/lDZ0zhlS1+i1yhXK4s20Kv+dhQ89Vg7tw+o0INHdL7VMXit7SuKIR5oKIo6SrncHkd/7FH/HuORP3exC1UtbBrXSDUjXd30uv4G9hMVaYHG7/eTFDzxtFOKg65DpFWYCM2qIuFL2lcUUj6HrEVdLV9r+Gnb9UOYr57jhK12nbe9WqTZGP7XdXGrXtctyFyeGgKtuXfmjIc+ZuqT/qQtFbGlc0/K5TYeVkVKqlEArjMTdf0Ja4HMXMloVDSryzvdXXv+/+XgbFAZxMmTAr/eiWpqKNGvuNjAzqIr3S0rii4XedvvqxWTVjwXmldS68ZzNt1z3kmSoalkbq97rXtYqCW3GGfS/d78WPAdVAOZzjhSnslqaM/UZGMHVh0VfbjVAr1MN18uyVPqCDQUq3rxwIDNJHCeLH6Q7p1evefYzC6x3F3dLq8tUHZd0UvtdCufz65dfSZ28Uj2iCZdtJ0dHRoRs2bKi2GEYKmbLo/kgDQZzcdi9r2Rl/5zcz1m88ntdQEKdVQWtExel2O4W9D4FYbim3G8hx+USVy6h9RGSjqnZ4vVYXFr0xcojqO4/SkjhOEH9tVzerN3YPU/IXnzJpsFAqjDgpm5BbQOIoaAuiGn7UhY/eqH8cX3p3fsxfGEe3NIW2K47TztjLzaL4V+h6ETczRsBa+hqJYBZ9Faj3Kt6g91fMey+0hJVDLpPxzRne3N8/pKzf8ZWHVZ/GqQVIIoU3bmaMkus7X8/fFaMy1KWiT7Mirfcq3qD3B8HBUT/8rGnHl17YZXJ/dsA3h/09k8YNybV3V5M2inDebG/3RxIpvEHVvH4Vqz192SGB5oX3bGbJuq2e82QNw49Q142ITBWRTa5/b4jIVSKyTES2icjTInKviLT47P+iiGzJ71v2CGvap9qETU6qdZas2+r7/op972HWtLvL5Jv7+30bmwH85oU9rO3qZm1XN+3XPzRkItSAKqs3dnt+V5JI4fU7Rpx8CCfDKI3fbSO9hCp6VX1eVdtUtQ2YDfQC9wIPAyeq6kzgtxwaGu7F3PwxPCPCSZJ2RVrPVbxru7p9Jx/t6ukr+r1H9aX7DdZw47hDrlmzxdOK9vuuJDFez+8Yr0ecFuVFmr7bRnqJ67qZB7ygqi8BL7mefww4PzGpSiDtirSeq3iDFM7RAemOYe89qi896mccNobP7zhJZLV4HcOvMlaESNZ+obxpdl0a1SFu1s2FuAaEu7gMeMBnHwUeEpGNInKF34FF5AoR2SAiG3bvjp7JUEgxg6ErST1X8QYp2oXzpxb93qNa00l9xpX+rvhdl4tPnhSpOrewpUKaXZdGdYhs0YvIaOBsClw0IvIPQD9wm8+uf6mq3SLyNuBhEdmmqr8q3EhVVwIrIVcwFVWuQqrdVTGMWqlOLcYqDAo2uvct5r1HsaYXzp86bPh1IU2ZRsZkGnyDn5kGqfh3Jeg74e4k2RKQYeRgnVwNL+K4bj4IPKWqrzpPiMgngQ8D89SnxFZVu/P//1FE7gVOAoYp+qSoBUWa9sKWYjODwloHO/uX6707x/XLuAG46dxccZNv4VL06XyJ4nddgubYen230+66NKpDHEV/ES63jYicCfxP4P9V1V6vHURkLNCgqn/K/30GcH0J8kYi7Yo07RRrFaZhkQ3qGtman0Xr4DVpKzugqbZ+w77b9RwDMoonko8+r6RPB9a4nv434Ahy7phNIvLt/LZHi8jP8tu8Hfi1iGwGngDuV9V/T0x6oyzEbQ3g7v4IVH34epRYQGd7KwcTnI+aFuo5BmQUTySLXlX3AUcWPPfffLbdBZyV//sPwKwSZRzRVCODIqpVmNbir6h3FvVo/abhrspIH9a9MsV4NcFqyjQWNSSkHOeN2/0xbVTr+hpGObDulTVKtTIoolqFtR74q6b1a7nuRiUxRZ9iqqlIowS068H1UY3AfVpdXkb9Ym2KU4wVf9UnaW/TYdQfpuhTTNoVaRL9X+qBsLm0hdS6y8uoPcx1k2JqIYNipNcsFOOGqQeXl1FbmKJPOSNdkaadYgLmaW/TYdQfpugNowSKccPUwp2aUV+YojeMEijWDWN3akYlsWCsYZRA2gPmhgFm0RtGSZgbxqgFTNEbRomYG8ZIO+a6MQzDqHNM0RuGYdQ55roxjBCsAZlR64Ra9CIyNT9YxPn3hohcJSITRORhEfld/v/xPvtfmt/mdyJyafJvwTDKhw3bNuqBUEWvqs+rapuqtgGzgV7gXmAR8AtVfRfwi/zjIYjIBGAxcDK5WbGL/RaEkUjcHilG5bEGZEY9ENdHPw94QVVfAs4Bfph//odAp8f284GHVXWPqu4FHgbOLFbYesIsxdrAGpAZ9UBcRX8hhwaEv11VX8n//V/k5sMW0gq87Hq8M//cMETkChHZICIbdu/eHVOs2qMWLcWReAeS9lbRhhGFyIpeREYDZwN3F76muXmEJc0kVNWVqtqhqh0TJ04s5VA1Qa1ZiiP1DsQqX416II5F/0HgKVV9Nf/4VRF5B0D+/z967NMNHOt6fEz+uRFPrVmKtXgHkgTWc9+oB+KkV17EIbcNwDrgUmBp/v+feOzzIPAVVwD2DOCaIuSsO2qtVW2t3YEkiVW+GrVOJIteRMYCpwNrXE8vBU4Xkd8Bp+UfIyIdIvJdAFXdA3wZeDL/7/r8cyOeWrMUa+0OxDCMQ0jOvZ4uOjo6dMOGDdUWw3BROEkJcncgaV6cDGMkISIbVbXD6zWrjDUiYV0aDaN2MUVvRMZ81YZRm5iiN4wRhPXtGZmYojeMEUJhnMWphQBM2dc51qbYMEYII7UWwjCL3kgAcwfUBiO5FmKkYxa9URIjtTVCLWK1ECMXU/RGSZg7oHawvj0jF3PdGCVh7oDawWohRi6m6I2SOLqliW4PpW7ugHRitRAjE3PdGCVh7gDDSD9m0RslYe4Aw0g/puiNkjF3gGGkG3PdGIZh1Dmm6A3DMOocU/SGYRh1jil6wzCMOscUvWEYRp2TylGCIrIbeKmMpzgK+D9lPH4S1IKMUBty1oKMYHImSS3ICMnKeZyqTvR6IZWKvtyIyAa/2YppoRZkhNqQsxZkBJMzSWpBRqicnOa6MQzDqHNM0RuGYdQ5I1XRr6y2ABGoBRmhNuSsBRnB5EySWpARKiTniPTRG4ZhjCRGqkVvGIYxYqhbRS8iZ4rI8yLyexFZ5PH6+0XkKRHpF5HzqyFjXo4wOb8gIs+KyNMi8gsROS6lcl4pIltEZJOI/FpETkibjK7tzhMRFZGqZGVEuJafFJHd+Wu5SUQuT5uM+W0+lv9ubhWR2ystY16GsGt5s+s6/lZEelIq5yQRWS8iXfnf+lmJCqCqdfcPaAReAP4cGA1sBk4o2GYyMBP4EXB+iuWcCzTn//4ssCqlcv6Z6++zgX9Pm4z57Y4AfgU8BnSk9Fp+Evi3anwnY8j4LqALGJ9//LY0ylmw/f8H3JpGOcn56j+b//sE4MUkZahXi/4k4Peq+gdVfQu4EzjHvYGqvqiqTwMHqyFgnihyrlfV3vzDx4BjKiwjRJPzDdfDsUClgz+hMub5MvBPwP5KCuciqpzVJIqMnwa+oap7AVT1jxWWEeJfy4uAOyoi2VCiyKnAn+X/HgfsSlKAelX0rcDLrsc788+ljbhy/jXwQFkl8iaSnCLytyLyAvDPwOcqJJtDqIwi8h7gWFW9v5KCFRD1Mz8vfwt/j4gcWxnRBoki47uBd4vIoyLymIicWTHpDhH595N3eU4BHqmAXIVEkXMJcImI7AR+Ru7uIzHqVdHXHSJyCdABLKu2LH6o6jdU9Z3A3wPXVlseNyLSAHwNuLraskTgPmCyqs4EHgZ+WGV5vBhFzn3zAXKW8ndEpKWqEgVzIXCPqg5UWxAfLgJ+oKrHAGcB/zv/nU2EelX03YDbCjom/1zaiCSniJwG/ANwtqoeqJBsbuJezzuBzrJKNJwwGY8ATgR+KSIvAqcA66oQkA29lqr6mutz/i4wu0KyOUT5vHcC61Q1q6rbgd+SU/yVJM738kKq47aBaHL+NXAXgKr+JzCGXB+cZKh0YKJCwY9RwB/I3ao5wY/pPtv+gOoFY0PlBNrJBXLelebr6ZYP+AiwIW0yFmz/S6oTjI1yLd/h+vuvgMdSKOOZwA/zfx9FzjVxZNrkzG83DXiRfN1QSj/zB4BP5v8+npyPPjF5K/6mK3hxzyJnZbwA/EP+uevJWcUA7yVnlewDXgO2plTOnwOvApvy/9alVM5/AbbmZVwfpGSrJWPBtlVR9BGv5U35a7k5fy2npVBGIecKexbYAlyYxmuZf7wEWFoN+WJczxOAR/Of+SbgjCTPb5WxhmEYdU69+ugNwzCMPKboDcMw6hxT9IZhGHWOKXrDMIw6xxS9YRhGnWOK3qhpRKQz34lyWv7xRBF5PN8F8L+LyEdF5DkRWV/COX4ZVlglIi+KyLACl3xXz0/k//6B0ylVRL7rdPgUkf9VrGyGEQVT9EatcxHw6/z/APOALararqr/Qa7i8NOqOjfKwURkVJLCqeq3VfVHHs9frqrP5h+aojfKSqJfasOoJCJyOPCX5Fo53yci95JrqNaUt8Dvzb/+PRFZB3wJ+Ba5nkH9wBdUdb2IfBI4FzgcaMw36Po+MAvYBjS5zvktcsV2TeR6pyx2ifQ/ReSDQB/wcVX9vYgsAd5U1eUFsv8S+CJwfl7eTeSKpF4A9qjqivx2NwJ/VNV/SeCSGSMUU/RGLXMOub73vxWR18j1/f5HchWvfwcgInOBL6rqBhG5GlBVnZF39TwkIu/OH+s9wExV3SMiXwB6VfV4EZkJPOU65z/kt2kEfiEiMzXX7hrg9fyxPwGsAD4c9gZUdZGI/J2qtuXlnQysAVbkm1pdSK7NrWEUjblujFrmInIN1Mj/f1HAtpCz7n8MoKrbgJfItdsFeFhV9+T/fr9ru6eBp13H+JiIPEVu6MZ0cqXrDne4/v+LuG8mf74XgddEpB04A+hS1deKOZZhOJhFb9QkIjIBOBWYISJKzppXcu6PYtgX4ZxTyLlb3quqe0XkB+S6DDqoz99x+S65KVP/D3BrCccxDMAseqN2OR/436p6nKpOVtVjge0MbQdbyH8AFwPkXTaTgOc9tvsV8PH8dieSGzkJuQlA+4DXReTtwAcL9rvA9f9/xngvWRHJuB7fS6475HuBB2McxzA8MYveqFUuIjcS0M3q/HPf89nnm8C3RGQLuWDsJ1X1gIgUbvct4Psi8hzwHLARQFU3i0gXuQDty+S6DboZLyJPAwcIdyO5WQk8LSJPqerFqvpWPh20R9M7KMOoIax7pWGkjHwQ9ingo6r6u2rLY9Q+5roxjBSRL6L6PfALU/JGUphFbxiGUeeYRW8YhlHnmKI3DMOoc0zRG4ZhcWEy2QAAAB1JREFU1Dmm6A3DMOocU/SGYRh1jil6wzCMOuf/ApPNc0sITJfZAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fullData.plot(x=\"Education\", y=\"Life Expectancy\", style=\"o\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "EoAFCquyhOQg",
        "outputId": "438c69fe-40a8-49c2-a082-4b1a65c652e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f417610abe0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fullData.plot(x=\"Consumer Opportunity Profile\", y=\"Life Expectancy\", style=\"o\")"
      ],
      "metadata": {
        "id": "oLj-8j8phzGF",
        "outputId": "d57003db-67ec-4fb1-f0e5-5b8130671517",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f41760e2bb0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fullData.drop(['Census Tract'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "4VWASfwFDQGM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_set, test_set = train_test_split(fullData, test_size=0.2, random_state=42)\n",
        "\n",
        "train_labels = train_set['Life Expectancy']\n",
        "test_labels = test_set['Life Expectancy']\n",
        "\n",
        "train_set = train_set.drop(['Life Expectancy'], axis = 1)\n",
        "test_set = test_set.drop(['Life Expectancy'], axis = 1)"
      ],
      "metadata": {
        "id": "Li8DpcuodcmX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "num_training = train_set._get_numeric_data()\n",
        "numeric_columns = num_training.columns\n",
        "cat_training = train_set.drop(numeric_columns, axis = 1)\n",
        "\n",
        "num_pipeline = Pipeline([\n",
        "        ('imputer', SimpleImputer()),\n",
        "        ('std_scaler', StandardScaler()),\n",
        "    ])\n",
        "\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "num_attribs = list(num_training)\n",
        "cat_attribs = list(cat_training)\n",
        "\n",
        "full_pipeline = ColumnTransformer([\n",
        "        (\"num\", num_pipeline, num_attribs),\n",
        "        (\"cat\", OneHotEncoder(handle_unknown = 'ignore'), cat_attribs),\n",
        "    ])\n",
        "\n",
        "train_prepared = full_pipeline.fit_transform(train_set)\n",
        "test_prepared = full_pipeline.transform(test_set)"
      ],
      "metadata": {
        "id": "--fcUQEge2au"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Linear Regression Model"
      ],
      "metadata": {
        "id": "ez6ulAWCMYCb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(train_prepared, train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lua68tWkg_uz",
        "outputId": "2620f31b-49fe-457a-cc08-28959e02354d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "lin_pred = lin_reg.predict(test_prepared)\n",
        "mean_squared_error(test_labels, lin_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YDkYb-YhTCg",
        "outputId": "abdb8169-021d-4c19-d3e5-e6068c5b153f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.334781473847382"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(lin_reg, train_prepared, train_labels,\n",
        "                         scoring=\"neg_mean_squared_error\", cv=5)\n",
        "lin_rmse_scores = np.sqrt(-scores)\n",
        "print(\"Scores:\", lin_rmse_scores)\n",
        "print(\"Mean RMSE:\", lin_rmse_scores.mean())\n",
        "print(\"Standard deviation:\", lin_rmse_scores.std())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HEWl8Y9BWuBD",
        "outputId": "7f0dbdd8-45f2-4f8a-e6a8-e6a2fb15fe32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores: [2.6518325  2.50999144 2.29471603 2.30367589 2.50364828]\n",
            "Mean RMSE: 2.452772829082511\n",
            "Standard deviation: 0.13616084734967435\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(lin_pred, test_labels)\n",
        "plt.xlabel(\"Predicted Life Expectancy\")\n",
        "plt.ylabel(\"True Life Expectancy\")\n",
        "plt.title(\"Linear Regressor Prediction\")"
      ],
      "metadata": {
        "id": "CyV2bCNOiBKH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "69916481-907a-42fd-aa27-67e233f3dc76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Linear Regressor Prediction')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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DechxHDzhrhrSmoNaxWzU7uXiZZYu8EfSOOB9wDeBzUQO5q/myfTT399vAwMDzRbDcZwy1DuKqVnE5Yv0dHe13OBA0goz64/dl0ZBSNoP+ChwJJGz+Erg7cCHzCy2VEYzcAXhNIO8d2RONsxasDzWDNbX21OXcOBGUU5BpPVBDAHfZduktrskzaqfmI7TerRbDoQru/S0W/RaHGmimN5vZr+L22Fmx9VZHqcDaeVOqZ2yePOs7PL4HWm36LU40jipT5PUW/gQchO+mKFMTgfR6st+ttMoMq8Jf3n9jrSKI70W0swgjjCzzxc+mNl6SUcCX8hOLKdTaPUReDuNImtRdlmO8CsprmbNLDoh/yKNguiStH3B9yCpB9g+W7GcTqHVR+DtlAMxVmVXq2mqknJJ+i4U2mmmSazd8y/SmJiuBH4m6WOSPgbcAnw/W7GcTqHV6+i0Uw7EWE0mtZim0piPkr4LXVIuTWLtRMUZhJl9WdL9RLWUAP6PmXldJKcutMMIvF1GkWM1mdQyC0xjYkz6jiSVJG+V2WcrkKoWk5ndDNycsSxOB5JXO24eo2YawViUXS1+mDTKJek7snDp6rbx/+SVNHkQxwFfBl5NVGBPgJnZThnL5nQIeRuB5zncM4/UMgtMq1ySviOtPvvMO2l8EP8KHGNmO5vZTma2oysHp53Ja7hnXqnFD1NLqGg7+X/yShoT0+/N7JHMJXGcnNDqkVXNYKyzwFpNjHmbfbYbaRTEgKRFwBJgy9rRZrY4M6kcp4m0U25DKY3wrVTbhnfy+SWNgtgJ2Ei0qlsBI1pC1HHajkZEVjXDCd4I30oz/DedGlDQCNKEuX60EYI4Tl7IOrKqWU7wRmStNzoz3gMKsiVNFNME4GPAm4EJhe1m9vcZyuU4TSVLs0ezyos0wrfSaP9Nq5dqyTtpTEw/AB4FZgMXAKcAqZzWkuYRLSNqwANEa0p8F+gHRoC7gX8ws5GYczeHcwDWmNkxadp0nLzTLCd4I3wr1bZRq3mo0wMKsjavpQlzfb2Z/Quwwcy+DxwFHFjpJEl9wKeAfjN7C9AFnERUumNvYF+gh6J1qEsYNrMZ4eXKwWkbmlVepBHVR6tpox5VWlu9VEstNKLKbRoFURjdD0l6C7AzUdJcGsYDPZLGAxOBtWZ2kwWIZhCvrVZox2llGlkmunjN5IVLV3P8AX1b8gZ6e7qZ0D2OeYtW1W095WpyE+qRb9IJJbeTaES+ThoT06WSJhGV974BeBXwL5VOMrNBSRcBa4BhYJmZLSvsl9QNfAj454RLTJA0AGwCFpjZkriDJM0F5gJMnTo1xe04TnNpVHmROAfudSsGufC4fQFqdu4mmTfS+m/qYR7Ka6mWRtAI81oaBfEzM1sP3A7sBSBpWqWTglI5FphGtGTpNZI+aGY/DId8A7jdzH6RcIk9gpLZC1gu6QEze6z0IDO7FLgUojWpU9yP04K0WyhjvZzg5Z5LpRFmLc7dekQP9U7sZv3GUe5Heid2pzq/QKfmUTTCp5TGxHRdzLZrU5x3OPC4ma0LTujFwNsAJJ0LTAY+nXSymQ2Gv78DbgNmpmjTaUPyuqJYs6n0XMqNMGsdfdbDvGEJw7mk7c62NMK8ljiDkLQ3UWjrzqFgX4GdKAp3LcMa4CBJE4lMTIcRZWWfRhQRdZiZvZLQ9iRgo5m9JGlXYBZRTSinA/FQxngqPZdKI8xaRp9pFUy5Gc5zw6NnD+W2O9vSCPNaORPTdOB9QC9wdNH254GPV7qwmd0l6VrgXiI/wkoiU9AG4EngV5IAFpvZBZL6gdPN7DTgTcC3Jb1CNMtZYGYPV3tzTnvQCaGMYzGhVXoulTLCa8kWT2PeqGSGaueSJo0ia/NaooIwsx8DP5b0N2b2q7Fc3MzOBc5N06aZDRBCXs3sl0RhsI7T9h3JWO35lZ5LmhHmWEefacqRVJrhtMNiUWloZf9ZGif16ZIeMbMh2GL+udgzqZ1GUc+OJI8/1rGa0NI8l3IjzFpGn2mUT6UZTidEILV6KZA0CmK/gnIAMLP1ktxh7DSMenUkef2xjtWE1uwOtpKCSTPza/cIpFb3n6VREOMkTQqhrkjaJeV5jlM36tGR5PXHWosJLc8dbKeYkMrR6v6zNB39xUQO5WvC5/cDX8pOJMfJhrz8WIvNXDv3dPPyps2jjmn1jrRwj8Mjm+mS2GxGX8YznDyaD1vdf5am3Pd/hIzmQ8Om4zyiyGlF6vFjrbUTKjVzDcWEdE6a2M25R7+55s6tWR1m6T1uNtui8LJUDnk0H7b6LCqtqWgXomJ9l0uaLGmamT2epWCOU29q/bHWoxOKM3OVMnG78XVRDs3qMCsl0WWhtPJqPmy2n6hW0qwHcS5Ree7pwOVAN/BDouQ1x2kZav2x1qMTSmPOqofJq5kdZpL8BSWVhdLKi/kwjjz7iSqRZgbxt0RlLu4FMLO1knbMVCrHyYhafqz16ISSzFylx9RKMzvMpHvskjJTWq1u688raWoxvRxKcxuApB2yFclx8kk91h6Iq59TTL3s05VkLS4DnlTqO80xcSTVCNqcUGSpHkqrk8t+Z0maGcTVkr4N9Er6OPD3wHeyFctx8kc9HI6lZq6de7qRYGjjSKLJayzO5jhZu7vEhpc2sedZNyLCiI+tpp6BJ5/l1kfXbZFrw8ubGNls2xxTfA9p77Eg88Klq1ON8tPcb9wxFx63b8va+vOKLEXpREnvBt4TPi4zs1sylWqM9Pf328DAQLPFcNqYRkcGlTqbIVJKSYvwJMnaO7GbF17cxMgryb/3YqWRRF9vD3ecdWiFo5LlqXQv9TrGSY+kFWbWH7cvbRTTA0TLgxbWlnYcpwHU4mwu+FuWrBzkzKvvSzTxFEhTZbsWc1Da2lBx93v+Tx7act64kFdResyZV9+3TTtO7aSJYjoNOAdYTjTI+JqkC8zse1kL5zh5ohmho7U6mwsyV1IOaanV6VspSCDJgb9+48iWxYWS7mWzWS5yH9qJNE7q+cBMMzvVzD4CHAB8LluxHCd/NGIN4FJqdYynybtIS8HfMlbndRq6oiUAxkzW/49OI42J6U9Ea0AUeD5sc5yOohmho7U6xtPK1jVOjIOyPopxgoEnn+W6FYOZzaLqMdOJm4XksQxHK5BGQfwWuEvSj4nMlMcC90v6NICZfSVD+RxnzNS7U0haQ7nesfalch9/QN+W6KJq7yNN3gXA5leMnSZ2M3G78YnHb3h5Mz+8c82o7cMjmznvhofq8qz7UspbDhE9wySndrFSg9bNcm4EFaOYQiZ1ImZ2fl0lqgGPYnIK1DvSZcnKQeZfc9+oEXZ3l1h4wl/VrVPJQu7S65XjiQVHMWvB8po76bHKHCdvd5fAtp3d9HR3cfwBfVx555pY53qXxCtmTOntYcNLm2JrXvX2dPPSplc6Phqq1iimL5vZiyUX3NXM/lgX6RwnA+pdamLh0tWx5pcd6lA3qbSdesodFzm08eVNsTOhwsi7HiazscqcFOkUt23OzL7YGQ1sNVWVU3RxSiMP9ZvyRBoFcbekuWZ2J4Ck44ELgTdmKpnj1EC9/QVJ5z0X08mkJc4EloWfozRyaMnKQeYtWjVq5G1EnXAls1SafAkYu8xJkU5x2+phkiolD/Wb8kKaKKZTiEJbF0q6Evg4W0t/l0XSPEkPSXpQ0lWSJkiaJukuSb+VtEjSdgnnnh2OWS1pdvpbctqZtBE09SiLUe/rFcs+84JlzL/mPgaHhjG22sV7J3bX3E4l5szsS+zg1w4NV3SAn3LQVPp6exBRBz2pATInUal0SRI93V1NlbtVqKggzOwBogWCTgcOAT5pZk9XOk9SH/ApoN/M3gJ0AScBXwYuMbPXA+uBj8Wcu0849s3Ae4FvSKr+W+A0hCzDHkvbOXvxA6M61bj26l2bp9brlcq+fuPIKJPV8MhmzKib3En/lyUrBxPDSaf09lQ0r/Tvscs2n4/ab7dEmbP+bsyZ2ceFx+27RWEl3dekid3bKLULj9uXc49+c8Vn3ajvdl5Jkyj3XeB1wH5EZqWfSvqamf3/Ka/fI2kEmAg8QzT7+EDY/33gPOCbJecdC/zIzF4CHpf0W+CtwK9StOk0kEYmj1Vjn693Hf4sSoXH8dzwCJecOCOz9bevGVjDLx97NnYGUdw5JpluJk3sHnXd61YMxkZaAQ35bhSbpJKc/OUWYEp61nldhKiRpPFBPACcFiq6Pi7pQKBiaKuZDUq6CFgDDAPLgBXAkJltCoc9DcQ96T7gzqLPScchaS4wF2Dq1KkpbsepJ41cd6Ba+3y96/BnUSq8lMIIPqv1t+947NnY47ukbaJ3kvIvzIi97q2PrhtVo2nWguUNX5OiWkVe7lnndRGiRpKoICTtZGZ/NrN/K95uZs9JqhjaKmkS0UxgGjAEXENkLqorZnYpcClEYa71vr5TnkYmj+VhydCxkiYfoZ7lqat9/q+YbfMckjraeYtWpW6vWWtS1GtgkOdFiBpFOR/EbYU3kn5Wsm9JimsfDjxuZuvMbARYTLQKXa+kgmJ6LRBn1BsEdi/6nHSc02Tq7QwuR739AINDw8xbtIovLMm+/mSc7N1dorene4td/PgD+li4dHVd7N3VPv+44+fM7OOOsw7l8QVHccdZhzJnZl9V/+9GfjeyoNXlrwflFESxt2eXMvuSWAMcJGmiJAGHAQ8DtwInhGM+Avw45twbgJMkbS9pGvAG4O4UbToNppELtZQ6JAvOxlr8AAZceeeazJ2PcbIvPOGvWHXue7jkxBlsfHkTP7xzTSoHfBqqje4ZHBpOpZSq+X+3+iI+rS5/PSjng7CE93GfR59sdpeka4mWKt0ErCQyBd0I/EjSF8O27wJIOoYo4ukcM3tI0tVECmUT8I9mVp+KY05dafSi7Fn4AQrx/6W5AvW+pzjZy2U61zNBrndiN88Nj1Cm1FIqJ2w1/+9GfzfqTavLXw8SS21IeprIGS1gHlsd0wLOMLPdY09sIl5qwylHuRISAh5fcBTQ2AVpKpW1KJYry3aKqWVRIKf1KFdqo5yJ6TvAjsCrit4XPl9WbyGdfNDOcd/zZ09PtI0W25UbWda7ksOzXvbuahyrneSEdcqTaGLKUxE+pzHkKe47KxPPwJPPjirwVmxXXrJyMHGkndRx1iJrueimetq701Z1LRxbwMtkdzZpSm04HUBhWcqsRs7VzEyqyZiuli/O2ZdLTpwR6+gutJtE3Gi+VlmTnMm9Pd1ceNy+AHWZ0SVFUXWP23ZOVaoss/o/OK1B2jWpnTam0rKUtZocqp2ZZJ2glOTorpTtvOGlTdusM1APWcs5Qus5o6u2Smo97i0tPkvJL64gnIodY6128Go7mmYlKFW6/tDwyKgOuh6yVqOwao1sSlslFRrzf8iTWdMZTUUTk6S/lPRdSTeHz/tIGlVgz2ldyv3g62EHr7ajaVaCUprrl5rcspS12Zm8jfg/NGOdbyc9aXwQVwBLgSnh86+BM7ISyGk8ST/40vo89b5+0vZmJSjNnz09Wr2sAoNDw1vs8NXKWo0vptmZvI34PzRbCTrlSaMgdjWzq4FXAEKhPU9aayOSOoKL/64+S2lW29HUmjFdEymreRWctdXIWm2pj2Zn8jbi/9BsJeiUJ82a1LcBxwO3mNn+kg4iWob0nQ2Qryo8UW7sZO0obAVHZLVrMadNKCvce7kkvUtOnLHFMV38nA7Ze/KoMtp5e2610MikRCeeWtek/jRRbaTXSboDmMzWWkpOA2hE51rv0thjvX4zFUm1Zo00x5crpVGgUOoDRq+fcN2KwbbuLL2cRb4pV2rj/WZ2TSiW9xQwnWiwszpUZ80d7TiD6KQRVrPvNWkG0SUlhgD3VejQ0s5KRHIym5e+cLJkrKU2zg5/rzOzTWb2kJk9mFfl0K50UpRH0r2eefV9DSn9kWTzP/nA3RMro1ZKHqtmoSB32Dp5o5yJ6U+SlgHTJN1QutPMjslOLKdArZ1GK9j+CyTdU2H0Pjg0zPxr7wOyiZEvZ+7o32OXRD9CudyEahYKSrq+O2ydZlFOQRwF7A/8ALi4MeI4pdSyilqrJSGl6UxHNhvn/+Sh1PJXqyDH6otJUm5xS3d2d4kdthvPc8Mjo2SKM01aMUgAABgtSURBVLF10voDTr4oV6zvZeBOSW8zs3UNlMkpImlt4DSdRqutqRt3r3Gs35jOylkvBZnG0ZyksDtp/QSn/Si3JvW/mdkZwPckjfLQuYmpMdTSaTTapl0czllw7FZy4hZTeq/VLjBeOlvY8NKmmhXkkpWDzLt6FeWiwSsp7GpmJVlHk9VCK5krnfpQzsT0g/D3okYI4iQz1k6jFvNUtZSOsov9BtWM2gv3umTlIGcsWhV7TG9Pd8X2y5mqqvHfzL/2vrLKoRoF2Mq0mrnSqQ/lTEwrwt+fl+6TtAgYtd3JF7WYp6qlXMG/sZi1ykVpve+vdmPWguXbjGQrFRwsxojCTyt17AuXrmZkc7J2qGf4ad5H561mrnTqw1iruf5NXaVwMqGRNu1Ko/J6JqFdt2Jw1Eg2rXIokGYEXEnmUkU7VhNbK4zOPQS3M8ms3Lek6cCiok17AecQKZfCL6sXGDKzGTHnPwE8T1T3aVNSIodTnmrNU2MdyVaKQCpn1oprM+l6XVLsSDYpmW3SxG4mbje+6vDUSvc0aWL3NufVYmJrhdF5I82VTn5ITJSTtH/C6wBgtBG4BDNbbWYzQud/ALARuN7MTizafh2wuMxlDgnHunJoALWsIJa0MhpUrm4a1+Yhe0+OTVpLymhO2n7Ufrtxx1mHJq5FXW4EnFTdtXucOPfoN2+zLY2JLYlWGJ03u3Cg0xzKzSDK5T48WmU7hwGPmdmThQ2SBPwd4DUEckItI9lic1Y1JpakNm99dB0XHrfvqJlFUjJZ0gzi1kejCO2xjIALMp//k4e2hNb29nRz3jFvHnU/tZjYWmF07iG4nUk5J/UhdWznJOCqkm0HA783s98kiQAsCyG23zazS+MOkjQXmAswderUOonbmdRrJCvgNTtPSNWBlGszyTwW53hPGr0Xrj9Wh31aE10tJrZGBhPUQp5DcJ1sSLMeRE1I2g44BrimZNfJjFYaxbzdzPYHjgD+UdI74g4ys0vNrN/M+idPnlwXmTuVWmrzj9U8VW2bSWsU9FW4TtZrG5QzsQFsfHlT4rNo6voXjlOGRqxJfQRwr5n9vrBB0njgOCLfRCxmNhj+/kHS9cBbgdszlrWjaUbW9ljarGZmUXydLEfApSY2iW3yJ9ZvHL2eden5rhCcvJH5DIL4mcLhwKNm9nTcCZJ2kLRj4T3wHuDBTKV0ahrJjtU8Va/Rcx5G4XNm9nHHWYfyxIKjmLLz6BlNu1bhddqXNCvKCTgF2MvMLpA0FXiNmd1d8eJR574mnPtc0fYrgDvN7FtF26YAl5nZkZL2Aq4Pu8YD/2lmX6rUXjuuBxFHI5Kqqm0jad2DVl3LoNZnPO2sG2NLhQh4fMFRdZPTcWql3HoQaRTEN4nWoz7UzN4kaRKwzMz+uv6i1kYnKIgsF9UpTvQS2y7PXKmNtHLVS7llqSTLFedLimIqpd0UptO+jHXBoAIHmtk/Ai8CmNl6YLs6yudUQVYLCBU7mYFRo99KbaQx8aR1ZC9ZOcisBcsTFwmqJV8jDeVyGoaGR5h/zX0V2/K8AacdSOOkHpHURegzJE0mmlE4TSCrpKo0tYzS+BMq1Taq5MhOU3Yi68zjSvc58opVbMvzBpx2II2C+Hcif8CrJX0JOAH4QqZSOYlklVSVRsFUWy4jbTJZ8fY0nX/WmcdpFi5K05ZHJjmtTkUTk5ldCXwWuBB4BphjZqU5DU6DyMp0UUnBjKVcRqkZJk3OQ5rOv5Z8jTRUymmoZ1uOk2cqKogQtbQR+AlwA7AhbHOaQFbhnHGdYqEKUaU20vpF0ii3NJ1/1vb9wjOOW3cColpM7ktwOoE0JqYbifwPAiYA04DVwJvLneRkRzWmi7TRPo1YuS5NG2kS5xph3y9euChNLSbHaUcqhrmOOkHaH/hfZnZaNiKNnU4Ic62GLENii6l3SGfeF89xnHaipjyIhAs+YGb71ixZnXEFsS2NisWPU0Td48SrJoxnaOPINp28d/614c/PqTflFERFE5OkTxd9HAfsD6ytk2xOhjRqnYFSk8/OPd1seHnTFrNMwWk98OSzsavBFV/DSaYVVp5z2os0Pogdi95vIvJJXJeNOE49aeQ6A8V+kVkLljM0PLLN/uGRzVx111Oj1myoNn+hHiPoVh2Ft8LKc057UVZBhAS5Hc3sMw2Sx6kjjV5noLhURxxJq76lndF8YckDXHnnmi1Z3sUjaEjntG7lUXgrrDzntBeJCkLSeDPbJGlWIwVy6kcjs3nL1S8qkLTqW9r1JoqVQ4Hhkc2c/5OHeHHklVSdfqVReNzsonBes2ccrbDynNNelJtB3E3kb1gl6QaiBX82FHaaWbm1pJ2cUO9s3iTzTKVSHT3dXRx/QN82PgiIYqcP2bvyQk8Ll66OrY4KbPF1FJNkeik3Co+bXcy/9j6wqLxGYVuzZhytsvKc0z6k8UFMAP5EtHZ0IR/CAFcQY6BV7N9JI+kk80w5M0fputTFMwEDrlsxSP8eu5R9DmMxo8SdU24UHqfkRjaPVkvNsvt7fSen0SSGuUp6GvgKWxWCinabmX0le/GqI+9hro3KS6iVJDm3Hz9ulPMZ2LLcZ1zH29vTzQ7bj9/SoW14aVPiNcqF3iaF7ArYuac79TXL/Q/mLVqVOEuJa9fXdXDagbGGuXYBr2JbxVCg+uQJp2WiUJLkTDIhrR0a5pITZ8TmQmx4eatCKFcAr2DiKQ6VldiSR3HI3pNjzVOnHDSV/j12SW16KTcKL+dgL8Xt/k4nUE5BPGNmFzRMkg6gVaJQqpVnSm9PbMe7sSgXohK9E7u36eSLZwSDQ8Nct2KQ4w/o49ZH1yWaV9KaXpL8MnE2/u4ubeODALf7O51DOQURN3NwaqBVolCS5Jw0sXubaCHYtrMs7XinnXVjqvZ6urswo6yTe3hkM7c+ui7RDFUPZ3zS7CJuW55mfI6TFeUUxGENk6JDaJUolCQ5zz06qs+YtrMsp2gmbjd+m2vMW7SqolyNmGklKRpXCE4nkqggzOzZWi4saTqwqGjTXsA5QC/wcWBd2P55M7sp5vz3Al8l8oVcZmYLapEnD7RKFEolOdPKW07RxOUnVLL/522m5TjtzpiK9VXdSJSRPQgcCHwUeMHMLqpw/K+BdwNPA/cAJ5vZw+XayXsUUyeSNqy3UqJdHqO9HKcdqKlYX504DHjMzJ6UUrk23gr81sx+ByDpR8CxQFkF4eSPtL6BuIJ/xVFMeZxpOU670ygFcRJwVdHnT0r6MDAAnGlm60uO7wOeKvr8NNHsYxSS5gJzAaZO9YXuWpks13BulQRFx8kTFZccrRVJ2wHHEJXqAPgm8DpgBtEa1xfXcn0zu9TM+s2sf/LkyiUbmsGSlYPMWrCcaWfdyKwFy0et1exkS9o1sx3H2ZZGzCCOAO41s98DFP4CSPoO8NOYcwaB3Ys+vzZsazkaUT3UR8flaZUERcfJG5nPIICTKTIvSdqtaN/fAg/GnHMP8AZJ08IM5CTghkylzIhynVM98NFxZVolQdFx8kamCkLSDkSRSMWF/f5V0gOS7gcOAeaFY6dIugnAzDYBnwSWAo8AV5vZQ1nKmhVZd05ZK6CxkiezWlJ4rIfNOk55MjUxmdkG4C9Ktn0o4di1wJFFn28CRuVHtBpZZ0/ncXSct0V5WiVB0XHyRiNMTB3N/NnT6enu2mZbPTunPI6O8zarmTOzjwuP25e+3h5EVOXVcyocpzKNCnPtWLLOnq40Om6GAzuPs5osQ2gdp11xBdEAsuycyimgZpl6WqUooeM45XEF0QYkKaBmhXe6zd9x2gNXEG1Ms0w9rVKU0HGc8riCaGOyNvWU82+4zd9xWh+PYmpjsoyg8gQ9x2l/XEG0MVmGd+YtlNVxnPrjJqY2JytTTx5DWR3HqS8+g3DGRB4T9BzHqS+uINqERtc+yjpD3HGc5uMmpjagGQlxHsrqOO2PK4g2oFkJcR7K6jjtjSuINqCZDmNfrMhx2hf3QbQBzXIYey6E47Q3riDagGY5jD0XwnHaGzcxtQFjdRjXah7yXAjHaW86XkG0iw29WodxXOTTvEWrGHjyWb44Z99U1/Cy3o7T3mRmYpI0XdKqotefJZ0haaGkRyXdL+l6Sb0J5z8R1q5eJWkgCxk72YYeZx4y4Mo716S+f8+FcJz2JjMFYWarzWyGmc0ADgA2AtcDtwBvMbP9gF8DZ5e5zCHhGv1ZyNjJNvQkM5BB6vv3pTwdp71plInpMOAxM3sSeLJo+53ACQ2SYRSdbENPMg9BdffvuRCO0740KorpJOCqmO1/D9yccI4ByyStkDQ36cKS5koakDSwbt26qoTq5HpC82dPRwn7OuH+HcepTOYKQtJ2wDHANSXb/zewCbgy4dS3m9n+wBHAP0p6R9xBZnapmfWbWf/kyZOrkq2TbehzZvZxykFTRymJTrl/x3Eq0wgT0xHAvWb2+8IGSacC7wMOMzOLO8nMBsPfP0i6HngrcHs9Bev0ekJfnLMv/Xvs0nL33y6RZ46Td5TQP9evAelHwFIzuzx8fi/wFeCdZhZrE5K0AzDOzJ4P728BLjCz/yrXVn9/vw0MZBLw5OSE0vBciGY97hx3nLEhaUVSIFCmJqbQub8bWFy0+evAjsAtIYT1W+HYKZJuCsf8JfB/Jd0H3A3cWEk5OJ1BJ0eeOU6jydTEZGYbgL8o2fb6hGPXAkeG978D/ipL2ZzK5NGU08mRZ47TaLwWkxNLXpMIOznyzHEajSsIJ5a8mnI6OfLMcRpNx9dicuLJqymn0yPPHKeRuIJwYknKtB4nsWTlYFM7ZM/edpzG4CYmJ5Y4Uw7AZrNc+CIcx8keVxBOLIVCfF0aXZAjD74Ix3GyxxWEk8icmX28kpBImYUvYsnKQWYtWM60s25k1oLlPktxnCbjCsIpS6PCSvMaVus4nYwrCKcsjQorzWtYreN0Mh7F5JSlUWGleQ2rdZxOxhWEU5FGhJX6+taOkz/cxOTkAs+Qdpz84TMIJxd4hrTj5A9XEE5u8Axpx8kXbmJyHMdxYnEF4TiO48TiCsJxHMeJxRWE4ziOE4srCMdxHCcWWUIxtlZE0jrgyWbLkcCuwB+bLcQYaVXZW1VuaF3ZW1Vu6FzZ9zCzyXE72kpB5BlJA2bW32w5xkKryt6qckPryt6qcoPLHoebmBzHcZxYXEE4juM4sbiCaByXNluAGmhV2VtVbmhd2VtVbnDZR+E+CMdxHCcWn0E4juM4sbiCcBzHcWJxBVFnJE2XtKro9WdJZxTtP1OSSdq1mXLGUU52Sf8k6VFJD0n612bLWkqS7JJmSLozbBuQ9NZmy1qKpHnhuT4o6SpJEyRNk3SXpN9KWiRpu2bLGUeC7FdKWh22fU9Sd7PlLCVO7qJ9/y7phWbKV46EZy5JX5L0a0mPSPpUXRozM39l9AK6gP8hSkQB2B1YSpTMt2uz5UsrO3AI8N/A9mHfq5stXxWyLwOOCNuPBG5rtnwlsvYBjwM94fPVwKnh70lh27eATzRb1ipkPxJQeF2VN9mT5A7v+4EfAC80W84qn/lHgf8AxoXtdfmN+gwiWw4DHjOzQnb3JcBngVaIDCiW/RPAAjN7CcDM/tBUySpTLLsBO4XtOwNrmyZVMuOBHknjgYnAM8ChwLVh//eBOU2SrRKlsq81s5ssANwNvLapEsYzSm5JXcBCot9onhklO9Fv9AIzewXq9xt1BZEtJxGNoJB0LDBoZvc1V6TUbJEdeCNwcDB5/FzSXzdRrjQUy34GsFDSU8BFwNlNkyoGMxskkmsNkWJ4DlgBDJnZpnDY00Qjx1wRJ7uZLSvsD6alDwH/1RwJ4ykj9yeBG8zsmWbKV44ysr8OODGYUW+W9IZ6tOcKIiOCzfgY4BpJE4HPA+c0V6p0FMseNo0HdgEOAuYDV0tSk8QrS4zsnwDmmdnuwDzgu82SLQ5Jk4BjgWnAFGAH4L1NFSolcbJL+mDRId8AbjezXzRDviQS5P4w8H7ga82UrRJlnvn2wIsWldv4DvC9erTnCiI7jgDuNbPfE2n3acB9kp4gmnLfK+k1TZSvHMWyQzSCXRysBncDrxAVB8sjpbJ/BFgc3l8D5M1JfTjwuJmtM7MRIllnAb3BhADR92WwWQKWIU72twFIOheYDHy6ifIlESf3+cDrgd+G3+hESb9tooxJJD3zp9n6Pb8e2K8ejbmCyI6TCWYOM3vAzF5tZnua2Z5E/8z9zex/milgGbbIHlhC5KhG0huB7chv1ctS2dcC7wzvDwV+03CJyrMGOEjSxDArOwx4GLgVOCEc8xHgx02Srxxxsj8i6TRgNnBywSaeM+Lk/oqZvaboN7rRzF7fVCnjiX3mFP1Gib7vv65HY55JnQGSdiD6R+5lZs/F7H8C6Dez3HWycbIHs833gBnAy8BnzGx586SMJ0H2twNfJTKTvQj8LzNb0TwpRyPpfOBEYBOwEjiNyOfwIyLT3krgg4UggTyRIPsGoki958Nhi83sguZIGE+c3MXPV9ILZvaqZslXjoRn3gNcCUwFXgBOr4e/0xWE4ziOE4ubmBzHcZxYXEE4juM4sbiCcBzHcWJxBeE4juPE4grCcRzHicUVhNMQJG0OFVUflFTILh/rta6QdEJ4f5mkfcoc+y5JbxtDG08opuJu3HZJx0g6K7yfHEqSrJR0cMq2rpD0uLZWov1ltfKOBUmnSprSiLac1sQVhNMohs1shpm9hSiX4vTinUVZw1VhZqeZ2cNlDnkXIbs3K8zsBjNbED4eBjxgZjOrLDExPzyfGWaWqbxFnEpUrsFxYnEF4TSDXwCvD6P7X0i6AXhYUpekhZLukXS/pH8ACLXuvx7WGPhv4NWFC0m6TVJ/eP9eSfdKuk/SzyTtSaSI5oWR+cFhhH9daOMeSbPCuX8haZmiOvuXEZWqTkUYiX9d0gzgX4FjQ3s9kt4j6VdBrmskpU6+kvRVSeeE97Ml3S5pXJhxfCsUZvu1pPeFY2KfX9j3OUkPhGezIMzA+oEri2Q9J5z7oKRLQ6Zu4Rl/WdLdob2Di9q7KBx/v6I1Qw6VtKSo3XdLuj7tPTs5o9H1zP3VmS9CfX2ijOYfExXRexdR1u20sG8u8IXwfntggKiG1XHALUTrPEwBhoATwnG3EXV0k4Gniq61S/h7HlHmd0GO/wTeHt5PBR4J7/8dOCe8P4qoTPioNTuAJ0q3E43Evx7zflfgdmCH8PlzhTZKzr+CqMb/qvC6MmyfCDxEVEJhNfC6ouP/i2iA9wai0i0Tyjy/I4BfAhNLns1tRBn9FG8P738AHF103MXh/ZHAf4f3nyAqST6+cD6RYn0UmFz0vI9u9vfPX2N7jWla7zhjoEfSqvD+F0RVVd8G3G1mj4ft7wH2K/gXiNZveAPwDuAqM9tMVLc/rszHQUSVQx8HMLNnE+Q4HNhHW4vR7hRG9e8gUkSY2Y2S1o/xPktl2ge4I7S3HfCrhGPnm9m1xRvMbKOkjxMpmXlm9ljR7qstqnP0G0m/A/Ym+fkdDlxuZhvDdZOezSGSPkukmHYhUk4/CfsKheBWAHuG94cD37JQlrxwXUk/AD4o6XLgb4APJ7Tn5BxXEE6jGDazGcUbQqe5oXgT8E9mtrTkuCPrKMc44CAzezFGlnoj4BYzO7mGa+wL/InRvoLSGjlG8vObXVHQaMnNbxDNKJ6SdB7RrKRAoU7RZir3G5cTKZYXgWts67oWTovhPggnTywFPqGwhrGkNyoqwHc70WIoXZJ2Y2vVymLuBN4haVo4d5ew/Xlgx6LjlgH/VPgQ/AaENj4Qth0BTKrD/dwJzJL0+nDdHRRVw02FpD2AM4GZwBGSDiza/f7gj3gdsBeRCSrp+d0CfFQhcizh2RSUwR/DjKowCynHLcA/KAQYFK5rZmuJquh+gUhZOC2KKwgnT1xGVOr6XkkPAt8mGq1eT1Sm+2GidXdHmWnMbB2RDX6xpPuARWHXT4C/LTipgU8B/cGp+jBbo6nOJ1IwDxGZmtaUkfN+SU+H11eSDgoynQpcJen+IPfeCYcv1NYw11WSticyw30mdLgfAy4LI32CfHcDNxNV7nyRhOdnZv8F3AAMBDPfZ8I1rgC+Fba9RLTQzINEiuaeMvdf4LIgx/3hmX+gaN+VwFNm9kiK6zg5xau5Ok6LIekK4KelPos8IenrwEozy9UKfk51uA/CcZy6ImkFkW/pzGbL4tSGzyAcx3GcWNwH4TiO48TiCsJxHMeJxRWE4ziOE4srCMdxHCcWVxCO4zhOLP8PiGkQ7fu/2AcAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decision Tree Regressor"
      ],
      "metadata": {
        "id": "KtMAS6OeMyj4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "tree_reg = DecisionTreeRegressor(random_state=42)\n",
        "tree_reg.fit(train_prepared, train_labels)"
      ],
      "metadata": {
        "id": "wehWfbRqMtdF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52a5d905-7cd3-4068-e77f-61f2adc1f58f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(tree_reg, train_prepared, train_labels,\n",
        "                         scoring=\"neg_mean_squared_error\", cv=5)\n",
        "tree_rmse_scores = np.sqrt(-scores)\n",
        "print(\"Scores:\", tree_rmse_scores)\n",
        "print(\"Mean RMSE:\", tree_rmse_scores.mean())\n",
        "print(\"Standard deviation:\", tree_rmse_scores.std())"
      ],
      "metadata": {
        "id": "LUekZxVUPv5a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c5239af-f31f-458b-a2bf-2a3f5b2e2f76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores: [2.99406418 3.48834498 3.45801945 3.28995374 3.33024188]\n",
            "Mean RMSE: 3.3121248462669315\n",
            "Standard deviation: 0.1756821836537344\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(tree_reg, train_prepared, train_labels,\n",
        "                         scoring=\"neg_mean_squared_error\", cv=5)\n",
        "tree_rmse_scores = np.sqrt(-scores)\n",
        "print(\"Scores:\", tree_rmse_scores)\n",
        "print(\"Mean RMSE:\", tree_rmse_scores.mean())\n",
        "print(\"Standard deviation:\", tree_rmse_scores.std())"
      ],
      "metadata": {
        "id": "0dEHKjb5M5na",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f672acf6-de04-4aa1-d5a1-c163fb3dc995"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores: [2.99406418 3.48834498 3.45801945 3.28995374 3.33024188]\n",
            "Mean RMSE: 3.3121248462669315\n",
            "Standard deviation: 0.1756821836537344\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tree_pred = tree_reg.predict(test_prepared)"
      ],
      "metadata": {
        "id": "sreVyMejW6DI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(tree_pred, test_labels)\n",
        "plt.xlabel(\"Predicted Life Expectancy\")\n",
        "plt.ylabel(\"True Life Expectancy\")\n",
        "plt.title(\"Decision Tree Regressor Prediction\")\n",
        "plt.xlim([70, 90])\n",
        "plt.ylim([70, 90])"
      ],
      "metadata": {
        "id": "UhH7xynrNt1y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "e1d45a17-79e1-46f2-dcf4-2f927fa4342f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70.0, 90.0)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest Regressor"
      ],
      "metadata": {
        "id": "nVoU7MbCPXFF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#For Random Forest Regressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "forest_reg = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "forest_reg.fit(train_prepared, train_labels)"
      ],
      "metadata": {
        "id": "eJi-tMcaPZbE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ad34511-3a35-4da9-ac44-64fe32dfba2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_forest_pred = forest_reg.predict(test_prepared)\n",
        "forest_mse = mean_squared_error(test_labels,random_forest_pred)\n",
        "forest_rmse = np.sqrt(forest_mse)\n",
        "forest_rmse"
      ],
      "metadata": {
        "id": "JrUHhMohPfbJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47bc5194-849f-48ab-edd1-37a321e92561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.1723752829431167"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(forest_reg, train_prepared, train_labels,\n",
        "                         scoring=\"neg_mean_squared_error\", cv=5)\n",
        "forest_rmse_scores = np.sqrt(-scores)\n",
        "print(\"Scores:\", forest_rmse_scores)\n",
        "print(\"Mean RMSE:\", forest_rmse_scores.mean())\n",
        "print(\"Standard deviation:\", forest_rmse_scores.std())"
      ],
      "metadata": {
        "id": "SkdTfXV9QUgo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc46ac15-7b32-4cd8-fd26-2d299283734e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores: [2.41143299 2.34655256 2.28742723 2.21720675 2.47893222]\n",
            "Mean RMSE: 2.348310347027794\n",
            "Standard deviation: 0.09159098094365435\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = [\n",
        "    # try 12 (3×4) combinations of hyperparameters\n",
        "    {'n_estimators': [1, 2, 3, 4, 5, 6, 7 ,8, 9 , 10, 11, 12, 13, 14, 15, 20, 25, 50, 75, 100], 'max_features': [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 21]}\n",
        "  ]\n",
        "\n",
        "forest_reg = RandomForestRegressor(random_state=42)\n",
        "# train across 5 folds, that's a total of 90 rounds of training\n",
        "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
        "                           scoring='neg_mean_squared_error',\n",
        "                           return_train_score=True)\n",
        "grid_search.fit(train_prepared, train_labels)"
      ],
      "metadata": {
        "id": "nUbJt2yiFfBx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "865726f3-fe23-4d3b-cfcf-f87aac963a74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
              "             param_grid=[{'max_features': [2, 4, 6, 8, 10, 12, 14, 16, 18, 20,\n",
              "                                           21],\n",
              "                          'n_estimators': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
              "                                           12, 13, 14, 15, 20, 25, 50, 75,\n",
              "                                           100]}],\n",
              "             return_train_score=True, scoring='neg_mean_squared_error')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search.best_params_"
      ],
      "metadata": {
        "id": "wr1Lkli8Gckl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef74a33e-2ef4-4728-8ad1-a5196e5e506d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_features': 12, 'n_estimators': 100}"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#For Random Forest Regressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "forest_reg = RandomForestRegressor(n_estimators=100, max_features=12, random_state=42)\n",
        "forest_reg.fit(train_prepared, train_labels)"
      ],
      "metadata": {
        "id": "wiPcPe8zG3cG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a2bf7ca-5e4c-4fd5-e109-a3e644bf6e8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_features=12, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_forest_pred = forest_reg.predict(test_prepared)\n",
        "forest_mse = mean_squared_error(test_labels,random_forest_pred)\n",
        "forest_rmse = np.sqrt(forest_mse)\n",
        "forest_rmse"
      ],
      "metadata": {
        "id": "rO7D1NNbG_tV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e00f2aa-acde-400a-848d-1eeebfa66896"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.1631659334159603"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(forest_reg, train_prepared, train_labels,\n",
        "                         scoring=\"neg_mean_squared_error\", cv=5)\n",
        "forest_rmse_scores = np.sqrt(-scores)\n",
        "print(\"Scores:\", forest_rmse_scores)\n",
        "print(\"Mean RMSE:\", forest_rmse_scores.mean())\n",
        "print(\"Standard deviation:\", forest_rmse_scores.std())"
      ],
      "metadata": {
        "id": "NEtwt1psHE97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c833870-d9ce-48ec-c195-d2e2242299b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores: [2.41966412 2.26891582 2.23511756 2.09915495 2.34992757]\n",
            "Mean RMSE: 2.274556006468262\n",
            "Standard deviation: 0.10871059704021316\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(random_forest_pred, test_labels)\n",
        "plt.xlabel(\"Predicted Life Expectancy\")\n",
        "plt.ylabel(\"True Life Expectancy\")\n",
        "plt.title(\"Random Forest Regressor Prediction\")\n",
        "plt.xlim([70, 90])\n",
        "plt.ylim([70, 90])"
      ],
      "metadata": {
        "id": "uwQxHxv3QlL5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "eb22c5c1-05b4-48a1-e966-3a9edc3bdb74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70.0, 90.0)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Lu9Ls9bmzuhMjt3z04dQDN3M5o5p5c2Yk5lm0w9t6ksmq1HZw05WTH7mOTCR9RtK9ku6RdJmk8ZIulrRG0srwmZly7AckPRg+H8hTTmf00s5v6x1SRdsdJ09yG5lI6gY+BbzGzAYkXQGcGHbPM7OrShw7BTgL6AEMWC5psZk9nZe8zuilXd/Wh1KqV6RtL4VPYuXUSt4+k7FAl6SxwARgfcbj5gA3mtlTQYHcCByRk4yO05ZM6uqsaHsalTryHSeJ3JSJmfUB5wFrgSeAZ8zshrD7q5LulvQNSTskHN4NrIutPx62jUDSqZKWSVq2YcOGOl6B47QuvSv6+PvzySVPKrVy5VmG3hk95GnmmgwcC0wH+oErJb0XWAD8GRgHXAB8ATin2vOY2QWhH3p6erafqpWOk0JhJLEl5dser6t1Ru8qLrttHUNmdEicdMDufGXuvsPat3N4tNM65GnmOhRYY2YbzGwQuAZ4k5k9YREvAD8E9k84tg/YPbb+srDNcUY9SSOJOIWw5jN6V3HJrWu3+lCGzLjk1rWc0bsqsX1aP46ThTyVyVrgQEkTJAk4BLhf0q4AYdtc4J6EY5cAh0uaHEY4h4dtjjPqKTViiIc1X3bbusQ2xds9mdGpB7mZuczsNklXAXcCm4EVROaoX0qaCghYCZwGIKkHOM3MTjGzpyR9GbgjdHeOmT2Vl6yO006klUXpkIaFNWeN9vI6XE49KDs5lqTlwA+An7R6aK5PjuWMBtImtCrOj9lrwfWJCqVD4uFzj6rqvK5wtj8aOTnWCcBuwB2SfippTjBROY7TBLImWp50wO6Jx6dtL4WHDzvlyDxtr6QxwDuA/wGGiJzn32ol85OPTBxnOFmiubIwe+HSRNOaT+Pb/tRrZJLJZyLpdcCHgKOAq4FLgTcDS4HEciiO045sb6acr8zdtyrlUYyHDzvlKKtMgs+kH7gImB9CegFukzQ7T+Ecp5GkTZS17LGnuOmBDduNgqmGSudCcUYfWUYm7zazR5J2mNlxdZbHcWqilpFFWib4pbeupWAMboWZGJsxemrn6spOY8jigD9F0qTCSsj9+EqOMjlOVdTqJE4z2RR7FZtZaqRZjvB2rq7sNIYsyuRIM+svrITw4MrjCh0nZ2qtMVWJyaZZvoK0a/z05SuZvXBpLkqld0Ufsxcu5TOXrwTgGyfM5Jb5B7sicYaRxczVIWmHgq9EUheQVJzRcZpKrU7iJFOOGDkygeb5CkpdS60muCTzGZDoR6r2HM72S5aRyaXAbyR9RNJHiMrB/yhfsRyncmqtMZVkyjn5wGktVWqk3LVUa4JLM5+dvfheryjsZKLsyMTMvibpbqLaWgBfNjOvk+W0HPVwEhdPlNW7oo9f3PXE1j4nT+jkrKP3adpbedI1FlONCS7NfJZ2Hg8JdorJlGdiZr8EfpmzLI5TE/WuMZVUtuT5wS11kbVa4teYNtd7NSa4SpWDhwQ7xWTJMzkO+BqwC5EJWYCZ2Ytzls1xKqaeU/CWcug3019QuMa0Gl3VmODS8kgmT+jk+cEtHhLslCWLz+TrwDFmtpOZvdjMXuSKxBkNtHrWdz3DddPK0J919D4eEuxkIouZ60kzuz93SRynxWiHrO96jcTKmQhdeTjlyKJMlkm6HOgFCqVUMLNrcpPKcVqAg/aeOiz7HVrHxJNHFnw9TYTO6COLMnkxsIlotsMCRjQNr+Nsl/Su6OPq5X3DFImAd+3XuAdumsJIqyEG+Ywgtrfil04+ZAkN/lAjBHGcViLJ+W7ATQ9saMj5SymMRgYGNFpxOe1Llmiu8cBHgH2A8YXtZvbhHOVynKbSbOd7KYXRSNlaNaLNaT2ymLl+DDwAzAHOAU4GMjnkJX0GOIXopW4V0ZwoFwE9wCBwO/AxMxtMOHYoHAOw1syOyXJOx6kHzXa+l1IYWWSrl2kqD8XlZrPtkyyhwa8ws/8ANprZj4C3AweUO0hSN/ApoMfMXgt0ACcSlWfZG9gX6CJSNkkMmNnM8HFF4jSUtFDZejvfC0UUp8+/blihxjSlNWlCJxtf2Dxie1y2elYWrrVETTE+/e/2SxZlUhg19Et6LbATUQJjFsYCXZLGAhOA9WZ2vQWIRiYvq1Rox8mbRpRcL/VgTVJmnR3iuec30z8wfCA/eULnMNnSTFP/fu0q9lpwPS+ffx17LbieM3pXUY56K9VaKzs7rUsWM9cFkiYDZwCLgR2B/yh3kJn1SToPWAsMADeY2Q2F/ZI6gfcB/5bSxXhJy4DNwEIz601qJOlU4FSAadOmZbgcxylNsRnmGyfMzMUMk/ZgPXvxvaw86/CtbQpybHxhpCIBmDBu7DD50kxQG/+x7VxDZlxy61qAktP61rtETbN9UU5+ZFEmvwlzmPwO2BNA0vRyBwUFdCwwnWja3yslvdfMLglNvgP8zsx+n9LFHkEh7QkslbTKzB4ubmRmFwAXAPT09CRVC3eczDQyeintAdo/MEjvir4ReR/T51+XqZ80n0oSl922ruwc8fXMP2m2L8rJjyxmrqsTtl2V4bhDgTVmtiE42K8B3gQg6SxgKvDZtIPNrC/8fQS4GZiV4ZyOUxONNMOUeoCefsVdI/wIWf0XSaapNIasse9fjfJFOY0ndWQiaW+icOCdQrHHAi8mFiJcgrXAgZImEJm5DiHKpj+FKDLsEDNLLMEaRjWbzOwFSTsDs4lqhDlOruRlhkmKYJo3ZwafDrMXFjNkNmJElLXEfpJpan3wyxTTIdV0XZVSb7OZ0zqUMnPNAN4BTAKOjm1/FvhouY7N7DZJVwF3Evk9VhCZozYCjwF/VPRFvsbMzpHUA5xmZqcArwa+J2kL0ehpoZndV+nFOU6l5GGGSTOdnXvcvkye0MnTm0b6QWBkPkclD+Ji09QZvau2+kjinHTA7lVfV7V42ZbtE1mZYa6kN5rZHxskT0309PTYsmXLmi2G08aklXWvJZJr9sKliQqqOyiDUpNdCViz8O1VnbeYM3pXcdlt6xgyo0PipAN2L+svyYLnjbQ3kpabWU+t/WRxwJ8m6X4z6w8nngyc7xnwTjtQ6YMuDzNMKdNZod/Tr7gr0X9RT8f0V+buWxflEcfLrTgFsiiT1xUUCYCZPS3JneFOy1Ptg67eZphyprPCueo10VUj8XIrToEsymSMpMkhPBhJUzIe5zhNpZkPuviIaKeuTjo7xODQtpFHQVEUtxvfOYb+TYN1mXK4nqOrtP48b8QpkEUpnE/kLL8yrL8b+Gp+IjlOfaj1QVfJAznedtKETp57fjODWyLl0T8wSOcYMXlC5zBFAcNHI/0Dg3R1dtScJFlv01Op/jxvxCmQpQT9/4ZM9IPDpuM8ssppB2p50FXyQC5umxSdNbjFmDBuLCvO3DYt0OyFS3MZOWUZkVWiKEv1lzVc2dn+yZK0CDCFqNDjt4ENWTLgHafZ1JIgV0nyYlLbJIpHRHmZiMr1W2mxxXIBBD5HvAPZ5jM5i6hk/Azgh0AncAlRIqHjtCy1RGZV8qDP+vAvHhHlZSIq12+lvqQsAQSuPJwsPpN3EpUyuRPAzNZLelGuUjlOnaj2QVfJgz5LLaziEVHvir6ypeSrJcn0JKIRSFrOC6QrRTdlOVnIokz+YWYmyQAkTcxZJsdpOpU8QJPadnaIiePG8szAyMispMRIgDGCN0zbiUVLVvOZy1dW5PTfqasTia0O/nft180v7npia5XhQhxZX/8Aiq3HSRsReQkUJwtZlMkVkr4HTJL0UeDDwPfzFctxmkulpUuyti20S/KxbDG45eGntq5X4vSPl6bv6x/g8tvXRcORBAxGKJRyIw03ZTnlyBLNdZ6kw4C/A68CzjSzG3OXzHGaTCUP0KS2leZmJJHmyzh78b0lnf6FsOQ0ihXKmMbWe3S2Q7ImH64immK3MJe74zglqCY3I41i5dO7oi9xkqxKiaubjf8YYt5VdwFeBsWpjrKhwaFk/O3AccDxwK2SvC6X45SgVMTUQXtPTbNAJVLsy6h1bpW0cw8OmU+f61RNlpHJPGCWmf0NQNJLgD8AP8hTMMdpZ9JMWX39A1y9vC/RAZ5E5xiNiAKrZFSTRKlzx+X2asBOJWRJWvwb0RwmBZ4N2xxnVNC7oo/ZC5cyff51zF64NDW5L05aZFSHlCnBscDgFmPZY09tlaNgKquUrCOhgtyVJjY6TpaRyUPAbZJ+RvRScyxwt6TPApjZf+Yon+M0lWrrXKWFFleiSAoUJrW66YENJY/vHAODiXOXlh6NFOiIjYK8GrBTKVlGJg8DvWz7Pv4MWAO8KHwcZ7ul2jnh08qMdFeZ3X7prWvLmrfSFElW4g8DrwbsVEqWkcnXzOz5+AZJO5vZX3OSyXFahloeqmmhxaVmVkzDiExkSRNo1YvBLbZ15OHVgJ1KyTIyuV3SgYUVSe8icsCXRdJnJN0r6R5Jl0kaL2m6pNskPSTpcknjUo5dENqsljQn2+U42yPV+CzqRdrDM769EvniI5ZKGTIr6fuYPKEzdX9Wn0lBSdZSJNMZnWRRJicD/yVpkaRLgY+yrRx9KpK6gU8BPWb2WqADOBH4GvANM3sF8DTwkYRjXxPa7gMcAXxHUkdxO6f9qFQxNNsRXO6hWo18c2d1c8v8g/nmCTNH9F2OUuOSt79uV04+cNoIxVFITuxQtKd7UheTJ3Qm9hEv3viu/bq3HtMh8a79PAveSaesMjGzVUSTYZ0GHAR80swez9j/WKBL0lhgAvAEkSK6Kuz/ETA34bhjgZ+a2QtmtoYoCGD/jOd0WpRqHrzV+izqRbkS67XIV9z3pDAjY7VcvbyPnj2m8I0TZm4d+cSz3IfMtirCs47ep6ySvHp531az2pAZVy/v82guJ5UsJegvAvYCXkdUTuUXkv7LzP671HFm1ifpPGAtMADcACwH+s2sUC71cSDpVacbuDW2ntYOSacCpwJMmzat3OU4TaSaCKE8HcFZ8yhKlVWpVb5437MXLq0ps71wL2+ZfzBzZ3UnVgiOt4H0emIezeVUShYH/CrgFDMzYI2kA4Cy4cCSJhONMKYD/cCVRCarumJmFwAXAPT09OTnnXRqppoHb5ojeFKKmSYr1Yb8FiugSRM6E2dWrMZRXQ8FGe+j3P3OU0k6o49UM5ekFwOY2TeDIiGsPwN8KUPfhwJrzGyDmQ0C1xBNqDUpmL0AXgYkjZv7gN1j62ntnDYiizO7mHlzZiSafp57fnNNJpdqzFNJZrrnnt88Qr5qHdX1iJSK91HN/a7Hsc7opJTP5ObCgqTfFO3rzdD3WuBASRMkCTgEuA+4iajGF8AHiPJWilkMnChphzBF8CuJ6oM5bUw1EUJzZ3UzcdzIAXQhjLVaqnnzTlJAg1uMiePGjvCpAJkDDQpBCYW5RuJ0dXbw3gOn0ZmhrG/xvawlIsujuZxKKWXmin97p5TYl4iZ3SbpKqIZGjcDK4jMUdcBP5X0lbDtIgBJxxBFfp1pZvdKuoJI+WwGPmFmlacOOy1FtZMsPZPiRyj14C/nD6kmjyLtfM8MDLLyrMOHnTurCa24bbw0fHeQG+DyO9Ylnru4bbz/Wia18gmxnEoppUwsZTlpPbkDs7OAs4o2P0JCZJaZLSYakRTWv0oUReZsR1QzyVKlD/4sD/NqpqLNKkclzuuktgXlUHCSz164lMGhkT+5Donz3/P6kvezlkmtfEIspxJKmbl2kfRZSafHlgvrUxskn+MkmlwANv0j2W+SxR9SLuQ3qxxJCqgSE1qWtmlttpj5w95pGUqNTL7Pttpb8WWAC3OTyHESGN85ZoSCeHrTYKL5KMsDupQZrLCvr39gawmTghnp3OP2LWv6qWQklaWtlzZx2oFUZWJmWSK2nFFIo+a56F3Rx5d+fm9i6G2BJPNRuYdvKTMYDK+dVUjaK7Q597h9mTdnxtbrL4x24uevxISWpW01JjnHaTRZp+11HKD6/Ixaz1OK4pHIQXtP5dJb1w5z7MUfvuXMYGnnHBgc4ks/v5fnB7eUvP5KnNdZ2lbSn09o5TQLWY5VSBtNT0+PLVu2rNlibNckZVXDcIdxnudJIn7uJCUk4OQDp/GVuVHI7vT51yVGkBRCFKv5RdT7+qsh6dq7OjvK+oKc0Y2k5WbWU1rCJoYAAByZSURBVGs/WQo9Os5WGpUZnbW/YnNPWnTUTQ9s2Lqe5msYI7FTV3WZ9a2QGd7sOmbO6KasMpH0UkkXSfplWH+NpBGVfp3RQaMyo0v1VxhBJEVgZVF2adFhQ2Zs/MfmTAmCpeRtVsl8L4HiNJMsI5OLgSXAbmH9T8Cn8xLIaW0alRmd9sCf1NXJN06YyaML3761oGGcLMquEBZcKK8eZ3DI2HH82IrmGylXkv7Tl69k1jk3ZCq3X4sS8hIoTjPJokx2NrMrgC0AoeKvZ6OPUqrJz0jjjN5V7LXgel4+/zr2WnA9Z/Rui6hKOs83T5jJyrMOr0suyNxZ3WxJ8Rf2bxrklvkHZ55QqlxJetgWxpymIOoxb4uXQHGaSZZoro2SXkLwS4ZZF5/JVSqnpalHZvQZvau45Na1W9eHzLauFxzl1ZynksinciHEafvjdE/qymRmg9Il3OtR8t1LoDjNJIsy+SxRmZO9JN1ClP1+fOlDHKc0l92WXGvqstvWbVUm1ZJVCZXL30jaH6ezQ2x8YTPT51+39cFdTgFV6teo1N/hJVCcZpGqTCS928yuJJpa95+BGUS+z9WhpLzjDKOSHIehFBNT2vY8KPcmX7x/p65OpMgMNmlCJ889v3nrZFYFs9S79uvm6uV9qQqolF/Ds9yddiY1z0TSnWb2hsLfBstVFZ5n0jzS8jvSKtruteD6RMXRIfHwuUfVVa48zD6l8m3mzZnB2YvvHTFrYqmcD88RcZpFvfJMSpm5/ibpBmC6pMXFO83smFpP7mw/pOV3QHKW+EkH7D7MZ1LgpAN2H7GtWnpX9DHvqru2Vtzt6x9g3lV3DZMj6ZgsyqeUWapgaqpEkbm/w2l3So1MxgFvAH4MnFK838x+m69oleMjk+aRllUepzhL/IzeVVx22zqGzOiQOOmA3enZY8qwB+pBe0/lpgc2VFVGpFxdL4DJEzo56+h9tj78i0cHnWPEjuPH0r9pcNj500Ymk7o6h81t4jitTr1GJmXLqUiaamYbSjZqEVyZNI8s5U8ErFn49tT9WepxJZl+0kxEWep6QeRIX3T867dWCi5F4fwA8668i8Etw38/hb58ROG0C7mXU5H0zbD4A0mLiz+1ntjZvkhLMoxjUDIZLy1HI05SeZC0sNqsDA7Z1lFNOQYGhzh78b0sWrJ6hCKJ9+U4o41SPpMfh7/nNUIQp72J2/wLc5knjXlLVRnOGgZb3K4e5UIK5rEsxSX7BwZHONfrLY/jtBul5jNZHv6O8I1Iuhwo6TORNAO4PLZpT+BM4I1EYcYAk4B+M5uZcPyjwLNE2fab6zEMc/IlnuMQn2CqmLRkvKwP8+Jw2bTjJnV1svGFzYkjiGK6Osew6R+by7bLgofzOqORaqsGv7FcAzNbbWYzg6LYD9gEXGtmJ8S2Xw1cU6Kbg0JbVyRtxtxZ3SVLkiS9vc+bM6NskcWk8iBpZUTOPmYfTth/90xlUTYNbhnhrO/qrPzn4eVLnNFKo0rQHwI8bGaPFTZIEvAe4LIGyeA0gUqKD86d1c2O49Mtr2l1wErVC7vpgQ1VzU8CMGXiDhW1r6VOmeO0O6Uy4NMSFQVUOunDiYxUGm8BnjSzB1OOMeAGSQZ8z8wuSJHzVOBUgGnTplUolpM3lU45258SyisoOflUWhmRWvwX6/sH6M5gevPkQscp7YA/v8S+B7KeIOSrHAMsKNp1EqVHJW82sz5JuwA3SnrAzH5X3CgomQsgCg3OKpfTGCpNxkv1f0yobtKqLH6YtGCBgqxZc08cZzRTygF/UJ3OcSRwp5k9WdggaSxwHJEvJe38feHvXyRdC+wPjFAmTutTSfHBeXNmDMtaL/Dc85vpXdFX8UM7URl0iInjxvLMwODWxMjielqF0VNWZehzrzujnSxVg2slaQRyKPCAmT2edICkicAYM3s2LB8OnJOvmE4rMHdWd2Jdq8EtVlE59nh/UF4ZFGfeFxd8LHXe4qTJUuHPjrO9kqsyCYrgMOBjRbtG+FAk7QZcaGZHAS8Fro189IwFfmJmv8pTVicirzfsSvp9JiWHo1r/R7FCKSQVxs9fS+n2esxF4jjtTq7KxMw2Ai9J2P7BhG3rgaPC8iPA6/OUzRlJXm/Yaf0ue+ypxLpbaX4OA2Z+6QbOPmafiuTpXdE3rPRJX/8A864sXfCxEtJ8Mp686IwmyoYGK+K9ks4M69Mk7Z+/aE6jKfWGnUe/l966NnGa2lKlWfoHBpl35V0VTWf7xWvuHpG4OLjFOHvxvRVfSzG9K/pS81g8edEZTWTJM/kOUZLiSWH9WeC/c5PIaRr1mu0v6/HFEVRx09C5x+1Lh5If0wX/SRZ6V/SxaXBL4r5SJVGysmjJ6sRIMIEnLzqjiizK5AAz+wTwPICZPQ2My1UqpylUkmBYj36TKJiM5s7qZkuJitZZFVzeRRdLKUr3lzijiSzKZFBSB+FFUtJUIPlVz2lr0sqS1PqGndRvmmlIsNWEVUoJZVVQpZTO5CpzV7LI0e0mLmeUkUWZ/F/gWmAXSV8F/h/wf3KVymkKpcqS1Lvfkw+clqhQjG2jiXlzZtDZMbJV5xhlVnCllM5ZR++TqY9S5KWAHafdKDs5FoCkvYnqawn4jZndn7dg1eCTY7UXL59/XeL2+CRavSv6hs2YOKmrs6JorrS56U8+cBpfmbtvTfLHz+EJi0670og54AsnmkZU8ffn8W1mNnICb2e7pt4PzQmdYxKd4/HSKbXkfxSOh3znVi+WsXdFH7MXLnXl4owqsuSZXEdkfRAwHpgOrAZqtxE4bUO9c1BKRVllGCxXRK0KqRI8G94ZrZT1mZjZvmb2uvD3lUQ1sv6Yv2hOK1HvHJRSx6VlwLcDeeXqOE6rU3EGvJndKemAPIRxWpd656CUOq7gNK/VX9IM8srVcZxWJ4vP5LOx1THAG4D1uUnktCRpJU6qzUFJ66+Q7Ne7om9E9eD+gUE+fflKzl5871alUk8/Tj36qvd9cpx2IUto8Itinx2IfCjH5imU03rUIwS24JiePv86Nr6weUTYbyHKau6sbhYtWT2iDH2B/oFBFlyzijN6V7HgmlWJJVkqpeDriPf1mctXckbvqor68VBhZ7RScmQSkhVfZGafa5A8TotSa1RUsWO6uJTJ5AmdnHX0NhNWObPQwOAQl922jqEib31atd5yo44kX4cBl966lp49pmS+zkZEjzlOK1Jq2t6xZrZZ0uxGCuS0LpVERRX7O9JmMyzwfFFkV5YZEosVSYFiRZQlwqpUWZRKS8k3MnrMcVqFUmau28PflZIWS3qfpOMKn0YI57QnBX9HQZFAaUUCIyOe0rLfs1Dsn8gSYVXKp+HOc8cpTxafyXjgb8DBwDuAo8Nfx0mklL+jFH39A8xeuHTr9LyLjn99xfWzkvwTWSKs5s2Z4aXkHacGSvlMdgmRXPewLWmxQJ3TypztiVre5ItNUAVzUe+KPk6/4q5E01aHxBazVP9ElgirubO6WfbYU1x669phX253njtONkqNTDqAHcPnRbHlwsdxEqn1TT4pya9USfotZqxZ+HZumX9woq8ia4TVV+buyzdOmFn3QpeOMxooNTJ5wszOqbZjSTOAy2Ob9gTOBCYBHwU2hO1fNLPrE44/AvgWkVK70MwWViuL01jmzZkxIkcEomq/xTMeppE0uqk2h6OSCCt3njtOdZRSJtV5PwNmthqYCVtDjPuIStl/CPiGmZ2XeuKo/X8DhwGPA3dIWmxm99Uik9MYCg/jpOz1RUtWl43SgmQFMW/OjBEVgLOaoVxJOE6+lFImh9TxPIcAD5vZY0qZirWI/YGHzOwRAEk/JUqUdGXSJpR6eBcrhGLSFITncDhO65KqTMzsqTqe50Tgstj6JyW9H1gGnB6mAo7TDayLrT8OJNYDk3QqcCrAtGnT6iawkw9JCuGgvady0wMbMikIH2E4TmuSaXKsmk4gjSOq5bWPmT0p6aXAX4kiwr4M7GpmHy465njgCDM7Jay/j2gu+k+WOpdPjrX94hNQOU4+NGxyrDpwJHCnmT0JUPgLIOn7wC8SjukDdo+tvyxsc0YRBQXS1z8wLIPe5whxnNYjS9JirZxEzMQladfYvncS5bEUcwfwSknTw8jmRGBxrlI6NRMv5FhIPqylr0LhRRiZ2ORzhDhOa5HryETSRKKIrI/FNn9d0kyi58OjhX2SdiMKAT4q1AT7JLCEKDT4B2Z2b56yOrVR7xkGk0qgFONlThyndchVmZjZRuAlRdvel9J2PXBUbP16YET+idOalKp/VY0yqTZ82HGc5tAIM5czCqjnDIO9K/rKJjl5mRPHaS0a4YB3RgHlstMricZatGR1yeJv3R7N5TgthysTpy6Uyk6v1J9SajTz6MK311lyx3HqgZu5nLowd1Y35x63b2KRxCzzicRJ84V0u4/EcVoWH5k4dSMtO71Sf0otNbgcx2kOPjJxcidtpJG2vdQox3Gc1sRHJk4uxB3uO3V10tmhYSXpy400vAaX47QXrkyculPscO8fGKRzjJg8oZP+TYNeW8txtkNcmTh1J8nhPrjFmDBuLCvOPLxJUjmOkyfuM3HqTj0TGB3HaQ98ZOJURakkxGqn13Ucp33xkYlTMfGKvsa2JMRCleB5c2bQ1dkx7BgP7XWc7RtXJk7FlEtC9NBexxl9uDJxKsZ9Io7jFOPKxKmYckmI5cxgjuNsf7gycSqmnE+k0lpcjuO0Px7N5YygXLn4wnJaGzeDOc7ow5WJM4ys5eJLlTvx0GDHGX3kZuaSNEPSytjn75I+LWmRpAck3S3pWkmTUo5/VNKqcOyyvOR0hpNmojr9irsy+zzmzZlBZ8fwuRI7O+ShwY6zHZObMjGz1WY208xmAvsBm4BrgRuB15rZ64A/AQtKdHNQ6KMnLzmd4aSZoobMKnOiF0+VWGrqRMdx2p5GOeAPAR42s8fM7AYz2xy23wq8rEEyOBkoZYrK6kRftGQ1g1uGa4/BLeYOeMfZjmmUMjkRuCxh+4eBX6YcY8ANkpZLOjU3yZxhJEVqxcniRHcHvOOMPnJXJpLGAccAVxZt/3dgM3BpyqFvNrM3AEcCn5D01pT+T5W0TNKyDRs21FHy0Ukhe71DStyfxYle6WRYjuO0P40YmRwJ3GlmTxY2SPog8A7gZDNLtKabWV/4+xciX8v+Ke0uMLMeM+uZOnVqvWXf7uld0cfshUuZPv86Zi9cSu+KPubO6ub897y+6vpaXpvLcUYfjQgNPomYiUvSEcDngX82s01JB0iaCIwxs2fD8uHAOQ2QdVSRJQy4VL5JGrUc6zhOe6KUgUF9Oo8UwVpgTzN7Jmx7CNgB+FtodquZnSZpN+BCMztK0p5EoxGIFN5PzOyr5c7X09Njy5Z5FHFWZi9cmpgP0j2pi1vmH9wEiRzHaTSSltcjYjbXkYmZbQReUrTtFSlt1wNHheVHgNfnKZuTzVFeLhvecRwHvDbXqMYLNjqOUy9cmYxivGCj4zj1wmtzjWLaoWCjm9kcpz1wZTLKaeWCjVmLTjqO03zczOWk0ux8ETezOU774CMTJ5Vm54u0gpnNcZxsuDJxSlLKDFZviv0jkyZ08vSmwRHtvCyL47QerkycliDJP9I5RnR2iMGhbYm1XpbFcVoT95k4LUGSf2RwizFx3Fi6J3Uhosz8c4/b153vjtOC+MjEaQnS/CDPDAyy8qzDGyyN4ziV4iMTpyXwsvWO0964MnFagmaHITuOUxtu5nJagmaHITuOUxuuTJyWoZFhyI7j1Bc3czmO4zg148rEcRzHqRlXJo7jOE7NuDJxHMdxasaVieM4jlMzuSkTSTMkrYx9/i7p05KmSLpR0oPh7+SU4z8Q2jwo6QN5yek4juPUTm7KxMxWm9lMM5sJ7AdsAq4F5gO/MbNXAr8J68OQNAU4CzgA2B84K03pOI7jOM2nUWauQ4CHzewx4FjgR2H7j4C5Ce3nADea2VNm9jRwI3BEQyR1HMdxKqZRSYsnApeF5Zea2RNh+c/ASxPadwPrYuuPh20jkHQqcGpYfUHSPbWLmys7A39tthAZcDnri8tZX1zO+lGXmkW5KxNJ44BjgAXF+8zMJNnIo7JjZhcAF4RzLTOznlr6y5t2kBFcznrjctYXl7N+SFpWj34aYeY6ErjTzJ4M609K2hUg/P1LwjF9wO6x9ZeFbY7jOE4L0ghlchLbTFwAi4FCdNYHgJ8lHLMEOFzS5OB4PzxscxzHcVqQXJWJpInAYcA1sc0LgcMkPQgcGtaR1CPpQgAzewr4MnBH+JwTtpXjgjqKnxftICO4nPXG5awvLmf9qIuMMqvJZeE4juM4ngHvOI7j1I4rE8dxHKdmWl6ZtEtZlhJyLpL0gKS7JV0raVLK8Y9KWhWOrUuoXoVyni2pL7b9qJTjj5C0WtJDkkZUL2iAnJfHtj0qaWXK8Q25n+Fcn5F0r6R7JF0mabyk6ZJuC/fp8hAin3TsgtBmtaQ5DZbx0nDeeyT9QFJnyrFDsXu+OC8ZS8h5saQ1MRlmphzbsBJMKXL+Pibjekm9Kcc28n7+W5DxXkmfDtvyeXaaWdt8gA6iRMc9gK8D88P2+cDXEtpPAR4JfyeH5ckNlvNwYGzY/rUkOcO+R4Gdm3g/zwY+l6H9w8CewDjgLuA1jZSzaPv5wJnNvJ9EybRrgK6wfgXwwfD3xLDtu8DHE459TbiHOwDTw73taKCMRwEKn8uSZAztn2vQ9zFNzouB48sc27DfepqcRW2uBt7f5Pv5WuAeYAJRTuGvgVfk9exs+ZFJEe1SlmWrnGZ2g5ltDttvJcqZaRXi9zML+wMPmdkjZvYP4KdE/4e8GSGnJAHvYXjYebMYC3RJGkv0w30COBi4KuxP+34eC/zUzF4wszXAQ0T3uBEyrjez6y0A3E5rfDdHyJnxuEb/1lPllPRiov9/4sikgbwauM3MNoVn0G+B48jp2dluyiS3six1Ji5nnA8Dv0w5xoAbJC1XVCKmERTL+clgjvtBytC3le7nW4AnzezBlGMacj/NrA84D1hLpESeAZYD/bGXiLT71JD7mSSjmd1Q2B/MW+8DfpXSxXhJyyTdKinpwdMIOb8avpvfkLRDwuEN+26Wu59ED+ffmNnfU7poyP0kGpW8RdJLJE0gGonuTk7PzrZRJtpWluXK4n3hzaolYpzT5JT078Bm4NKUQ99sZm8gqhjwCUlvbbCc/wPsBcwk+oGcn+f5s1Li/16cDFtMQ+5nULrHEpmpdgMm0mJFSZNklPTeWJPvAL8zs9+ndLGHRSVB/gX4pqS9GiznAmBv4J+IzC5fyOP8WclwP8t9NxtyP83sfiLT+g1ELworgaGiNnV7draNMqF9yrIUy4mkDwLvAE4O/7wRhLcdzOwvRKX68zJ3JMppZk+a2ZCZbQG+n3L+VrmfY4mG65enHdTA+3kosMbMNpjZIFGC7mxgUpAT0u9To+5nkoxvApB0FjAV+GzawbF7+QhwMzArBxlT5TSzJ4I17gXghzT/u1nqfu4c5Lsu7eAG3k/M7CIz28/M3go8DfyJnJ6d7aRM2qUsyzA5JR0BfB44xsw2JR0gaaKkFxWWg5x5Vz8ulnPX2L53ppz/DuCVIVJpHJH5KddoFJLf8g4FHjCzx5MOaPD9XAscKGlC8OMcAtwH3AQcH9qkfT8XAydK2kHSdOCVRL6LRsh4v6RTiGzjJ4WXiBGE384OYXlnIkV5Xw4ylpKz8OATkQkp6X/ZyN96opxh3/HAL8zs+aQDG3w/kbRL+DuN6AXsJ+T17GxEVEGtHyLTwd+AnWLbXkI0udaDRFEKU8L2HuDCWLsPEzk2HwI+1AQ5HyKyPa4Mn++G7bsB14flPYmieu4C7gX+vQly/hhYBdwdvmy7FssZ1o8iert5uBlyhu0XA6cVbWvm/fwS8ADRQ+7HRNFZexIphoeITHQ7hLbHEJUHKhz77+FergaObLCMm8O5C9/NM0Pbrb8hojfuVeFergI+0oR7uTSc+x7gEmDHYjnDeiN/6yPkDNtvBo4oatvM+/l7ImV1F3BI2JbLs9PLqTiO4zg1005mLsdxHKdFcWXiOI7j1IwrE8dxHKdmXJk4juM4NePKxHEcx6kZVyZOQ4hVSr1H0pWhvEO1fV0s6fiwfKGk15Ro+zZJb6riHI+GPICy2yUdo1BBWdJURdWCV0h6S8ZzFVfF/UOl8laDpA9K2q0R53K2f1yZOI1iwMxmmtlrgX8Ap8V3xrLFK8LMTjGzUklfbyNkJ+eFmS02s4Vh9RBglZnNsvTyJEnMC/dnppnlKm+MDxLl5zhOzbgycZrB74FXhFHD7xXN6XCfpA5F87/cEYr6fQyizGdJ31Y098avgV0KHUm6WVJPWD5C0p2S7pL0G0kvJ1Janwlv/G8JI4erwznukDQ7HPsSSTcomvfhQqKy7JkIb/jfVjTPxteBY8P5uiQdLumPQa4rJe1YQb/fknRmWJ4j6XeSxoSRzHcVFQv8k6R3hDaJ9y/s+4Ki+V3ukrQwjOx6gEtjsp4Zjr1H0gUhu7twj78m6fZwvrfEzndeaH+3pH+VdLBi83hIOkzStVmv2Wlj8sy+9I9/Ch/CHA5Epbt/BnycaNSwEZge9p0KnBGWdwCWERXTO46oBHYH0Zt0P2F+C6KM4x6i+lLrYn0VsnrPJjZPC1E5iTeH5WnA/WH5/7ItA/ztRMXvRsyHQsI8KURv+N9OWN4Z+B0wMax/gYT5V4gy+tewLRP90rB9AlEG/0FEGfJ7xdr/iuhl8JVEFV3Hl7h/RwJ/ACYU3ZubgZ6YHFNiyz8Gjo61Oz8sHwX8Oix/nKjMfmG+nilESvgBYGrsfh/d7O+ff/L/VGVacJwq6NK2WRF/D1xEZH663aK5PCCq//O6gj8E2InoYflW4DIzGwLWS1qa0P+BRJVv1wCY2VMpchwKvCa8dAO8OIwW3kqktDCz6yQ9XeV1Fsv0GuCWcL5xwB9T2s4zs6viG8xsk6SPEimkz5jZw7HdV1hUT+tBSY8QVdVNu3+HAj+0UBuuxL05SNLniZTYFCJF9vOw75rwdznw8rB8KFF5oM3xfiX9GHivpB8CbwTen3I+ZzvClYnTKAbMbNh0q+EBuzG+CfhXM1tS1C5xCuEqGQMcaEWF+GLKpZ6IaIKhk2roY1+i+mTFvo3iOkhG+v0rOx2wpPFEpeh7zGydpLOJRjsFXgh/hyj/3PghkRJ6HrjSts3r4mzHuM/EaSWWAB9XmItc0qsUVf39HXBCsNHvSmT2KeZW4K2Kqu8iaUrY/izwoli7G4B/Laxo23zivyOaXwJJRxJNVVortwKzJb0i9DtR0quyHixpD+B0ohLlR0o6ILb73cF/shdRUcnVpN+/G4EPKUTQpdybguL4axipFUY3pbgR+JhC8EShXzNbTzTz4BlEisUZBbgycVqJC4kqnN4p6R7ge0RvwdcSVTi9D/hfEkxFZraByGdwjaS72DbXyc+BdxYc8MCngJ7gML6PbVFlXyJSRvcSmbvWlpDzbkmPh89/pjUKMn0QuEzS3UHuvVOaL9K20OCVisqUX0Tk71kPfAS4MIwgCPLdTjRz52lhpJV4/8zsV0SVoJcFU+PnQh8XA98N214gmsfmHiKldEeJ6y9wYZDj7nDP/yW271JgnUUTNDmjAK8a7DhthqSLiebMuKpc22Yh6dvACjO7qNmyOI3BfSaO49QVScuJfGGnN1sWp3H4yMRxHMepGfeZOI7jODXjysRxHMepGVcmjuM4Ts24MnEcx3FqxpWJ4ziOUzP/P0h0s0JreEiGAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "XGBooster Regression"
      ],
      "metadata": {
        "id": "gbzdBbjFRF7r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "\n",
        "xgboost_reg = xgb.XGBRegressor(objective=\"reg:squarederror\",booster='gbtree', random_state=42)\n",
        "xgboost_reg.fit(train_prepared, train_labels)"
      ],
      "metadata": {
        "id": "T8OWC1zHRNA0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bdc91b6f-df7a-4ac1-de19-0401f6cdda14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(objective='reg:squarederror', random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_pred = xgboost_reg.predict(test_prepared)\n",
        "xgb_mse = mean_squared_error(test_labels,xgb_pred)\n",
        "xgb_rmse = np.sqrt(xgb_mse)\n",
        "xgb_rmse"
      ],
      "metadata": {
        "id": "b635FSBLTEFg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4443b2cc-519b-4e3d-81e6-119585e79563"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.105799488625408"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(xgboost_reg, train_prepared, train_labels,\n",
        "                         scoring=\"neg_mean_squared_error\", cv=5)\n",
        "xgboost_rmse_scores = np.sqrt(-scores)\n",
        "print(\"Scores:\", xgboost_rmse_scores)\n",
        "print(\"Mean RMSE:\", xgboost_rmse_scores.mean())\n",
        "print(\"Standard deviation:\", xgboost_rmse_scores.std())"
      ],
      "metadata": {
        "id": "SNtjzEovTUjr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "826f4c0c-6250-4915-fe2b-4e1b8c97afd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores: [2.40006641 2.3419438  2.28706753 2.17596788 2.48450782]\n",
            "Mean RMSE: 2.337910688484338\n",
            "Standard deviation: 0.10409689782200965\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = [\n",
        "    # try 12 (3×4) combinations of hyperparameters\n",
        "    {'objective':[\"reg:squarederror\"],\n",
        "     'n_estimators': [1, 2, 3, 4, 5, 6, 7 ,8, 9 , 10, 11, 12, 13, 14, 15, 20,\n",
        "                      25, 30, 35, 40,45,50,55,60,65,70, 75,80,85,90,95,100],\n",
        "     'max_depth': [3,4,5,6,7,8,9,10],\n",
        "     'learning_rate': [0.01, 0.05, 0.1, 0.15, 0.2, 0.25]}\n",
        "  ]\n",
        "\n",
        "xgboost_reg = xgb.XGBRegressor(booster='gbtree', random_state=42)\n",
        "# train across 5 folds, that's a total of 90 rounds of training\n",
        "grid_search = GridSearchCV(xgboost_reg, param_grid, cv=5,\n",
        "                           scoring='neg_mean_squared_error',\n",
        "                           return_train_score=True)\n",
        "grid_search.fit(train_prepared, train_labels)"
      ],
      "metadata": {
        "id": "qKxy6UT2J084",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61d95346-c467-48ac-c3ec-1cfaa0149c76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=XGBRegressor(random_state=42),\n",
              "             param_grid=[{'learning_rate': [0.01, 0.05, 0.1, 0.15, 0.2, 0.25],\n",
              "                          'max_depth': [3, 4, 5, 6, 7, 8, 9, 10],\n",
              "                          'n_estimators': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
              "                                           12, 13, 14, 15, 20, 25, 30, 35, 40,\n",
              "                                           45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
              "                                           90, ...],\n",
              "                          'objective': ['reg:squarederror']}],\n",
              "             return_train_score=True, scoring='neg_mean_squared_error')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search.best_params_"
      ],
      "metadata": {
        "id": "CKj2Q1KzKmq5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fab7e619-6d11-46f8-e88f-bef9094eacd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 0.1,\n",
              " 'max_depth': 3,\n",
              " 'n_estimators': 70,\n",
              " 'objective': 'reg:squarederror'}"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "\n",
        "xgboost_reg = xgb.XGBRegressor(objective=\"reg:squarederror\",\n",
        "                               learning_rate=0.1, max_depth=3,\n",
        "                               booster='gbtree', n_estimators=70, random_state=42)\n",
        "xgboost_reg.fit(train_prepared, train_labels)"
      ],
      "metadata": {
        "id": "ek_drWZaLqXN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4834b719-ae83-4e79-8292-91314d8d8559"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(n_estimators=70, objective='reg:squarederror', random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_pred = xgboost_reg.predict(test_prepared)\n",
        "xgb_mse = mean_squared_error(test_labels,xgb_pred)\n",
        "xgb_rmse = np.sqrt(xgb_mse)\n",
        "xgb_rmse"
      ],
      "metadata": {
        "id": "qn4ITev7LvI6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b170b0db-7df1-4a06-be22-d885fe83b049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.1255615081945898"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(xgboost_reg, train_prepared, train_labels,\n",
        "                         scoring=\"neg_mean_squared_error\", cv=5)\n",
        "xgboost_rmse_scores = np.sqrt(-scores)\n",
        "print(\"Scores:\", xgboost_rmse_scores)\n",
        "print(\"Mean RMSE:\", xgboost_rmse_scores.mean())\n",
        "print(\"Standard deviation:\", xgboost_rmse_scores.std())"
      ],
      "metadata": {
        "id": "D2DH_5O8MKC8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2dedd2d0-39b2-45b2-d211-b66a6bff0e86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores: [2.35864995 2.33069122 2.252131   2.14921233 2.47792653]\n",
            "Mean RMSE: 2.313722206299127\n",
            "Standard deviation: 0.10965991623451203\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(xgb_pred, test_labels)\n",
        "plt.xlabel(\"Predicted Life Expectancy\")\n",
        "plt.ylabel(\"True Life Expectancy\")\n",
        "plt.title(\"XG Booost Regressor Prediction\")\n",
        "plt.xlim([70, 90])\n",
        "plt.ylim([70, 90])"
      ],
      "metadata": {
        "id": "kyxwa17oTlmd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "921d22a5-1ad1-4873-fe9a-037d7b6f620a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70.0, 90.0)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgboost_reg.get_booster().get_score(importance_type='gain')\n"
      ],
      "metadata": {
        "id": "jds_QqKT_O2F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b100633-5f91-470f-f7c1-e5927ecc56c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'f14': 99.04931519791666,\n",
              " 'f5': 119.47350796599996,\n",
              " 'f2': 75.39698975857141,\n",
              " 'f6': 14.936761535,\n",
              " 'f54': 71.33808055555555,\n",
              " 'f17': 15.149965603333333,\n",
              " 'f16': 15.111392189166667,\n",
              " 'f10': 19.242453636875002,\n",
              " 'f1': 12.127846798651424,\n",
              " 'f0': 21.254073971350355,\n",
              " 'f81': 22.3950195,\n",
              " 'f7': 18.711493323333332,\n",
              " 'f8': 18.7507201725,\n",
              " 'f37': 21.0273438,\n",
              " 'f15': 21.427869916000002,\n",
              " 'f12': 24.924772024,\n",
              " 'f80': 30.991699195000002,\n",
              " 'f3': 20.99609412473684,\n",
              " 'f46': 16.007297995000002,\n",
              " 'f70': 28.33278162857143,\n",
              " 'f9': 20.192418381176466,\n",
              " 'f4': 19.631354212142856,\n",
              " 'f41': 16.865134775714285,\n",
              " 'f11': 21.962671158,\n",
              " 'f13': 31.37685776666667,\n",
              " 'f50': 16.0137634,\n",
              " 'f68': 13.036385866666668,\n",
              " 'f18': 12.211205,\n",
              " 'f43': 11.74209095,\n",
              " 'f24': 22.986412,\n",
              " 'f34': 19.1548805}"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    }
  ]
}