EDA_fake_job_postings.ipynb

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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "drive_dir = '/content/drive/Shareddrives/MATH 2327 3439 - Data/'"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading the Data"
      ],
      "metadata": {
        "id": "SiZih9ZiNCy5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(drive_dir + 'data/fake_job_postings.csv')"
      ],
      "metadata": {
        "id": "deXXGod4MvZG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Looking into the Data"
      ],
      "metadata": {
        "id": "D85GO_t8NLe2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "id": "M7dsOnLRNRDq",
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          "height": 556
        },
        "outputId": "afdf46d0-5713-428b-a6f3-6b4c661826b0"
      },
      "execution_count": null,
      "outputs": [
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              "   job_id                                      title            location  \\\n",
              "0       1                           Marketing Intern    US, NY, New York   \n",
              "1       2  Customer Service - Cloud Video Production      NZ, , Auckland   \n",
              "2       3    Commissioning Machinery Assistant (CMA)       US, IA, Wever   \n",
              "3       4          Account Executive - Washington DC  US, DC, Washington   \n",
              "4       5                        Bill Review Manager  US, FL, Fort Worth   \n",
              "\n",
              "  department salary_range                                    company_profile  \\\n",
              "0  Marketing          NaN  We're Food52, and we've created a groundbreaki...   \n",
              "1    Success          NaN  90 Seconds, the worlds Cloud Video Production ...   \n",
              "2        NaN          NaN  Valor Services provides Workforce Solutions th...   \n",
              "3      Sales          NaN  Our passion for improving quality of life thro...   \n",
              "4        NaN          NaN  SpotSource Solutions LLC is a Global Human Cap...   \n",
              "\n",
              "                                         description  \\\n",
              "0  Food52, a fast-growing, James Beard Award-winn...   \n",
              "1  Organised - Focused - Vibrant - Awesome!Do you...   \n",
              "2  Our client, located in Houston, is actively se...   \n",
              "3  THE COMPANY: ESRI – Environmental Systems Rese...   \n",
              "4  JOB TITLE: Itemization Review ManagerLOCATION:...   \n",
              "\n",
              "                                        requirements  \\\n",
              "0  Experience with content management systems a m...   \n",
              "1  What we expect from you:Your key responsibilit...   \n",
              "2  Implement pre-commissioning and commissioning ...   \n",
              "3  EDUCATION: Bachelor’s or Master’s in GIS, busi...   \n",
              "4  QUALIFICATIONS:RN license in the State of Texa...   \n",
              "\n",
              "                                            benefits  telecommuting  \\\n",
              "0                                                NaN              0   \n",
              "1  What you will get from usThrough being part of...              0   \n",
              "2                                                NaN              0   \n",
              "3  Our culture is anything but corporate—we have ...              0   \n",
              "4                              Full Benefits Offered              0   \n",
              "\n",
              "   has_company_logo  has_questions employment_type required_experience  \\\n",
              "0                 1              0           Other          Internship   \n",
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              "4                 1              1       Full-time    Mid-Senior level   \n",
              "\n",
              "  required_education                   industry              function  \\\n",
              "0                NaN                        NaN             Marketing   \n",
              "1                NaN  Marketing and Advertising      Customer Service   \n",
              "2                NaN                        NaN                   NaN   \n",
              "3  Bachelor's Degree          Computer Software                 Sales   \n",
              "4  Bachelor's Degree     Hospital & Health Care  Health Care Provider   \n",
              "\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 17880,\n  \"fields\": [\n    {\n      \"column\": \"job_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5161,\n        \"min\": 1,\n        \"max\": 17880,\n        \"num_unique_values\": 17880,\n        \"samples\": [\n          4709,\n          11080,\n          12358\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11231,\n        \"samples\": [\n          \"Environmental Lab Technician\",\n          \"Junior Designer\",\n          \"Case Handler\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3105,\n        \"samples\": [\n          \"US, VA, Arlington\",\n          \"US, AL, Dothan\",\n          \"US, CA, Long Beach\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"department\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1337,\n        \"samples\": [\n          \"Finance team\",\n          \"Product Ops\",\n          \"Engineering - Hardware\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary_range\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 874,\n        \"samples\": [\n          \"60000-130000\",\n          \"30000-450000\",\n          \"120000-130000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"company_profile\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1709,\n        \"samples\": [\n          \"WHY WORK AT IMAGINET\\u00a0\\u201cWorking at Imaginet is great! I get to work with leading edge technology as well as the some of the best developers, project managers and management in the industry.\\u201d\\u00a0-- Evan Bosscher, Application Developer at Imaginet\\u201cImaginet enables me to use the latest tools and platforms to solve complex and challenging problems and encourages me to continue growing in my application development career.\\u00a0 My peers at Imaginet are some of the best and brightest developers in the country.\\u201d\\u00a0-- Tyler Doerksen, Cloud Application Specialist at Imaginet\\u00a0At Imaginet, our clients look to us to help them with their toughest projects using the newest technologies.\\u00a0 We are the technology partner who tames their chaos, who turns disruption into steady state and makes the unknown more predictable.\\u00a0 We are at the forefront of emerging technology trends, new product releases and shifts in the software development industry.\\u00a0 We are constantly learning about, and experimenting with the latest and greatest software platforms, controls, processes and design patterns.\\u00a0Our current projects include:\\u2022\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Enterprise Mobility \\u2022\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Dev Ops \\u2022\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Business Intelligence \\u2022\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Scrum/Lean/Kanban \\u2022\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Enterprise Content ManagementIf you are passionate about being part of a leading edge software development team with a commitment to continuous learning, we want you as part of the Imaginet team.BalancePeople lead dynamic lives and not all personal commitments lie outside of 9-5.\\u00a0 At Imaginet, we recognize that our employees need the flexibility to support their families, hobbies, charities, and other interests to stay motivated and fulfilled.\\u00a0 Many employees work partially from home, start/stop work at different times or telecommute from a home office.\\u00a0 At Imaginet, it is passion and performance that counts, not which hours in the day you sit in the office.Personal DevelopmentWe want the best and brightest minds working with us.\\u00a0 Our employees are recognized thought leaders in software development and we encourage continued learning.\\u00a0Imaginet employees who wish to write exams are subsidized upon the completion of technical certifications and can receive raises for exemplary results.\\u00a0 We fund and encourage our employee\\u2019s participation at conferences with special interest in those who can speak on niche software development topics and practices. Imaginet has more Microsoft MVPs on staff per capita than any other Microsoft partner and we encourage employees to attend the MVP summits at Microsoft in Redmond.\\u00a0 Imaginet employees are very active in the local software community and lead .NET user groups in many of our employees residing cities.\\u00a0 We also encourage our employees to present internally via \\u201clunch and learns\\u201d on topics of interest.Compensation\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0Even though Imaginet is a great place to work, it\\u2019s still a job and you want to be paid.\\u00a0 We pride ourselves on our \\u2018promote from within\\u2019 philosophy and provide competitive compensations. We provide excellent health care benefits including eye, dental and disability.\\u00a0 We also offer overtime to our employees that want to work extra hours on select projects.Recognized LeadersImaginet is globally recognized within the software industry as a premier consulting and software development organization.\\u00a0 We are a multiple Microsoft Gold Partner, Telerik Gold Partner, #URL_eb33f3e87ac1203366e8701171cb1ef8a42857099f85613b3b7f029ffb751723# Partner and a founding member of the Lean Kanban University.\\u00a0\\u00a0 We are invited to speak at conferences around the world and have published numerous books under Microsoft Press.\\u00a0 In addition, we have many Microsoft MVPs, Virtual Technical Specialists (VTSs) and Regional Directors on staff.\\u00a0Our experience spans over 2,400 engagements, 1,100 clients, and over 17 years.\\u00a0 We are the world leaders in ALM on the Microsoft platform and 1st Ever Winner of the Global Microsoft Partner of the Year Award in ALM.\",\n          \"Bluesmart is an early stage startup that is re-defining the way that we travel. We are currently in stealth mode, but we are planning to unveil our first product to the world very soon! We are a group of seasoned entrepreneurs with experience building companies in retail, travel, mobile applications, and product design. We are highly international, with our team splitting time between New York and Buenos Aires. Join us, and we guarantee that you will learn a lot, meet interesting people, and have fun at the same time.\",\n          \"Softomotive Ltd is a Software Development company located in Athens, Greece. We specialize in Process Automation software and our customers range from individual users to large Fortune500 companies all over the world.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14801,\n        \"samples\": [\n          \"EvergiveEvergive was born from a simple desire to donate in search of an easy way to transact.From these simple roots, Evergive grew into a transformative platform that improves and enhances the entire fundraising ecosystem. Our goal is to help nonprofits be more successful in their mission by providing technology to support the donor lifecycle and to provide a better fundraising experience. Partners think of us as a mobile presence for their organization.Evergive is designed to keep individuals connected to the causes and organizations they support. Whether you support a church, a school, a community group or a friend's cause - Evergive simplifies and enhances giving.We are located near Caltrain in Palo Alto, CA and backed by some of Silicon Valley's top investors.Engagement Marketing ManagerThe right candidate for this position is a smart, outside-the-box thinker who is ready to create and implement marketing strategies and excited to analyze engagement to make smarter marketing decisions. We're looking for someone creative and independent who doesn't sleep until goals are accomplished.\\u00a0We love candidates who have overcome adversity and created something out of nothing.\\u00a0This is a position with a lot of potential for growth and ownership, helping define our brand, identity and strategy at our partner organizations.\\u00a0On a daily basis, you will...* Give updates on engagement per partner and progress against our goals* Keep in touch with the partners through calls and visits to make sure their program is on track* Construct creative marketing campaigns to increase awareness of the Evergive platform within our partner communities* Run user engagement at Evergive, constructing and testing the email notification system for donors* Writing the partner adoption playbook, ensuring that we track and measure how we launched a new account and never repeat something that doesn't work\",\n          \"To work on analytics modelling using desktop and programming tools (R, SPSS) on business data in a financial investment institution.\",\n          \"We\\u2019re hiring a talented full time in-house web developer to join our team at The Mobile Majority.Responsibilities include:Developing stunning web applications by writing pixel perfect code.Efficiently communicating current tasks, issues, and recommendations on front end development tasks.Effectively gathering user input and bug reports an push updates accordingly.The Mobile Majority is a rapidly growing ad tech startup based in Santa Monica,\\u00a0CA, with offices in New York City, San Francisco, Chicago, Sao Paolo and Ho Chi\\u00a0Min City. We have been honored as the 'Best Creative Technology' by the American\\u00a0Association of Advertising Agencies and won 'Best Series A' By the Los Angeles\\u00a0Venture Association.\\u00a0We have raised $21M, and do business with some of the biggest brands in the world (Chase Bank, Unilever, Paramount, Warner Brothers, State Farm Insurance, Hershey\\u2019s, Clorox and Time Warner Cable, to name a few).The Mobile Majority has a culture of engineering focused, data-driven decision-making, and demands business intelligence that is timely, accurate, and actionable. \\u00a0As a key member of a small and versatile team, you will be responsible to design, test, deploy and maintain software solutions.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"requirements\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11968,\n        \"samples\": [\n          \"Minimum LPN (Licensed Practical Nurse) current license requiredMinimum\\u00a05 years\\u2019 experience as an LPNSolid computer skills (Microsoft applications) a MUSTMust be detail orientedKnowledge of MedWatch REQUIREDKnowledge of prescription medication, dietary supplements and common side effects of bothKnowledge of medical terminologyDemonstrated experience with Nutrition, elder care and quality improvement preferredStrong communication skills to communicate information verbally and in writing so that others can understandCustomer service skillsAbility to file and retrieve informationAbility to keep information confidentialAbility to interface with multiple levels of leadershipAbility to work independentlyAbility to multitask and work independentlyAbility to sit for long periods of timeAbility to lift 5 pounds\",\n          \"Significant experience with direct recruiting processes (not in a staffing firm), interview methods, and reference-checking.Organized and detail oriented but capable of prioritizing actions based on known objectives in any project.Self-starter capable of demonstrating the resourcefulness needed in a startup environment where objectives will change regularly.A good communicator capable of writing and speaking concisely and professionally with colleagues and external stakeholders.Experience running a small business or an office in a B2B business service environment desirable.Good skills using web-based applications, Google Apps, Google Docs, MS Word, MS Excel, and capable of quickly learning new tools.Comfortable working in a remote environment where colleagues may be located in multiple locations nationally.\",\n          \"Associates-level of experience. \\u00a0Company Overview:Our international client specializes in\\u00a0hi-tech transactional and technology services, consulting, systems integration, and managed services.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"benefits\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6205,\n        \"samples\": [\n          \"Attractive pay packageMobile allowanceTransportation allowancePersonal loansFlexible hoursMedical insuranceSocial insurancePaid vacationsSocial activitiesTraining &amp; developmentTuition assistanceProfit ShareCequens Team \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0We believe in choosing the best\\u00a0calibers and assisting them in\\u00a0continuing to develop their\\u00a0potential and qualifications. A\\u00a0good working environment, the\\u00a0right motivation and great\\u00a0teamwork is the way we use to\\u00a0successfully achieve our goals.About usWe are\\u00a0a\\u00a0leading technology corporation that specializes in mobile messaging services and solutions. Serving hundreds of renowned customers in more than 40 countries worldwide, please visit our website\\u00a0#URL_414f2c0c30d37b5a8db0880b6d51f6b9b08fc03a22553f0d0a568b2687dc8e3e#\\u00a0to know more about us. \\u00a0\",\n          \"Competative Pay,Vacation,Sick Leave,Health Insurance\",\n          \"Health and Dental Insurance through UHC50% of premium paid by companyVision Plan through Advantica50% of premium paid by companyShort Term Disability through UNUM50% of premium paid by company.$25,000 A&amp;D life insurance paid 100% by the company.401K with company matching 100% up to the first 3%, and 50% on the 4% -5% contributions.\\u00a0 Immediately vested upon eligibilityEight paid holidays.\\u00a0 1 Floating holiday80 hours of PTO accrued during first year of employment.Flexible environmentFree drinks and snacksEmployee lounge with video gamesCasual dress (jeans)Plenty of fun activities\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"telecommuting\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"has_company_logo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"has_questions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"employment_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Full-time\",\n          \"Temporary\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"required_experience\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Internship\",\n          \"Not Applicable\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"required_education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"Vocational - Degree\",\n          \"Doctorate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"industry\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 131,\n        \"samples\": [\n          \"Publishing\",\n          \"Music\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"function\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"Human Resources\",\n          \"Business Development\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fraudulent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "id": "dcBYmJT4NTGm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf4603fd-dbaf-4851-9814-4903dff21ca8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17880, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique()"
      ],
      "metadata": {
        "id": "_87zyChmNNv-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cacaab18-1573-48fa-d72d-202998bba6e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "job_id                 17880\n",
              "title                  11231\n",
              "location                3105\n",
              "department              1337\n",
              "salary_range             874\n",
              "company_profile         1709\n",
              "description            14801\n",
              "requirements           11968\n",
              "benefits                6205\n",
              "telecommuting              2\n",
              "has_company_logo           2\n",
              "has_questions              2\n",
              "employment_type            5\n",
              "required_experience        7\n",
              "required_education        13\n",
              "industry                 131\n",
              "function                  37\n",
              "fraudulent                 2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Look at how many rows of missing data are there in each column"
      ],
      "metadata": {
        "id": "5eEeX-WWNZul"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "id": "pV_UyDUANVWh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "134cb47b-ee84-4935-8be3-79e6c0637d0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "job_id                     0\n",
              "title                      0\n",
              "location                 346\n",
              "department             11547\n",
              "salary_range           15012\n",
              "company_profile         3308\n",
              "description                1\n",
              "requirements            2695\n",
              "benefits                7210\n",
              "telecommuting              0\n",
              "has_company_logo           0\n",
              "has_questions              0\n",
              "employment_type         3471\n",
              "required_experience     7050\n",
              "required_education      8105\n",
              "industry                4903\n",
              "function                6455\n",
              "fraudulent                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at the percentage of rows that have missing data for each column"
      ],
      "metadata": {
        "id": "V6_RqLYlNhkE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum() / df.shape[0] * 100"
      ],
      "metadata": {
        "id": "5GrckKNYNWAg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ca12de4-672c-4b8f-b7d3-7071b7c57ddd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "job_id                  0.000000\n",
              "title                   0.000000\n",
              "location                1.935123\n",
              "department             64.580537\n",
              "salary_range           83.959732\n",
              "company_profile        18.501119\n",
              "description             0.005593\n",
              "requirements           15.072707\n",
              "benefits               40.324385\n",
              "telecommuting           0.000000\n",
              "has_company_logo        0.000000\n",
              "has_questions           0.000000\n",
              "employment_type        19.412752\n",
              "required_experience    39.429530\n",
              "required_education     45.329978\n",
              "industry               27.421700\n",
              "function               36.101790\n",
              "fraudulent              0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Looking at patterns around missing data"
      ],
      "metadata": {
        "id": "KU_fxtNH1kmK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fraudulent_df = df[df['fraudulent']==1]"
      ],
      "metadata": {
        "id": "v7W3emk31m9J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fraudulent_df.isna().sum() / fraudulent_df.shape[0] *100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjWZ-X071pXM",
        "outputId": "e8ceb691-c92f-45e6-8281-6c75f33d3377"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "job_id                  0.000000\n",
              "title                   0.000000\n",
              "location                2.193995\n",
              "department             61.316397\n",
              "salary_range           74.249423\n",
              "company_profile        67.782910\n",
              "description             0.115473\n",
              "requirements           17.782910\n",
              "benefits               42.032333\n",
              "telecommuting           0.000000\n",
              "has_company_logo        0.000000\n",
              "has_questions           0.000000\n",
              "employment_type        27.829099\n",
              "required_experience    50.230947\n",
              "required_education     52.078522\n",
              "industry               31.755196\n",
              "function               38.914550\n",
              "fraudulent              0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "real_df = df[df['fraudulent']==0]"
      ],
      "metadata": {
        "id": "ws1uD5Bs1q3f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "real_df.isna().sum() / real_df.shape[0] *100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DS8F79eD1sog",
        "outputId": "92b6372d-e5e3-4eed-a125-d3b7d9c6950b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "job_id                  0.000000\n",
              "title                   0.000000\n",
              "location                1.921947\n",
              "department             64.746679\n",
              "salary_range           84.453979\n",
              "company_profile        15.992712\n",
              "description             0.000000\n",
              "requirements           14.934760\n",
              "benefits               40.237452\n",
              "telecommuting           0.000000\n",
              "has_company_logo        0.000000\n",
              "has_questions           0.000000\n",
              "employment_type        18.984366\n",
              "required_experience    38.879746\n",
              "required_education     44.986482\n",
              "industry               27.201128\n",
              "function               35.958622\n",
              "fraudulent              0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "abs((fraudulent_df.isna().sum() / fraudulent_df.shape[0] *100)-(real_df.isna().sum() / real_df.shape[0] *100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCm6PYVa1zJI",
        "outputId": "bfeab42b-0635-4e75-db0e-0b6bfb8104fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "job_id                  0.000000\n",
              "title                   0.000000\n",
              "location                0.272049\n",
              "department              3.430282\n",
              "salary_range           10.204556\n",
              "company_profile        51.790198\n",
              "description             0.115473\n",
              "requirements            2.848150\n",
              "benefits                1.794881\n",
              "telecommuting           0.000000\n",
              "has_company_logo        0.000000\n",
              "has_questions           0.000000\n",
              "employment_type         8.844733\n",
              "required_experience    11.351201\n",
              "required_education      7.092040\n",
              "industry                4.554068\n",
              "function                2.955927\n",
              "fraudulent              0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# salary range\n",
        "plt.figure(figsize=(6,6))\n",
        "plt.bar(['Real', 'Fraudulent'], [84.453979, 74.249423])\n",
        "plt.ylabel('% Missing Data', fontsize = 16)\n",
        "plt.xlabel('Job Status', fontsize = 16)\n",
        "plt.ylim(0,100)\n",
        "plt.title('Salary Range', fontsize=18)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "J2zxO8Dc2zRQ",
        "outputId": "6435e835-247d-48c6-953d-12f707bbb2b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Salary Range')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# company profile\n",
        "plt.figure(figsize=(6,6))\n",
        "plt.bar(['Real', 'Fraudulent'], [15.992712, 67.78291])\n",
        "plt.ylabel('% Missing Data', fontsize = 16)\n",
        "plt.xlabel('Job Status', fontsize = 16)\n",
        "plt.ylim(0,100)\n",
        "plt.title('Company Profile', fontsize=18)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "XWpFo3Zt1s90",
        "outputId": "8342b022-8afc-467e-ca42-387d89619296"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Company Profile')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Looking for patterns around 'fraudulent' jobs"
      ],
      "metadata": {
        "id": "m4SnhMwPNvCp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at how many jobs in this data set are fraudulent"
      ],
      "metadata": {
        "id": "BcGvoMGmOFCd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['fraudulent'].value_counts()"
      ],
      "metadata": {
        "id": "RqJ9ZayGNyry",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e310c2d1-c1a0-40f4-d9e6-7122ba6b680d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    17014\n",
              "1      866\n",
              "Name: fraudulent, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "866/17880 * 100"
      ],
      "metadata": {
        "id": "MG5k7MYSOEOF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f61a82fc-77e2-47ac-ff7d-ffa00e549495"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.8434004474272925"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Grouping by Job Function to see if there is a relationship between particular job functions and whether or not a job posting is fraudulent"
      ],
      "metadata": {
        "id": "q54KtF3POIFi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['function'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h0fWDgbdLG-Z",
        "outputId": "69af2394-d3c0-44cc-cd56-e882a9ac1ff9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['function'].value_counts()"
      ],
      "metadata": {
        "id": "M4Pxg5HuNziE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "948a261a-0503-4728-8aa6-200dc013536e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Information Technology    1749\n",
              "Sales                     1468\n",
              "Engineering               1348\n",
              "Customer Service          1229\n",
              "Marketing                  830\n",
              "Administrative             630\n",
              "Design                     340\n",
              "Health Care Provider       338\n",
              "Other                      325\n",
              "Education                  325\n",
              "Management                 317\n",
              "Business Development       228\n",
              "Accounting/Auditing        212\n",
              "Human Resources            205\n",
              "Project Management         183\n",
              "Finance                    172\n",
              "Consulting                 144\n",
              "Writing/Editing            132\n",
              "Art/Creative               132\n",
              "Production                 116\n",
              "Product Management         114\n",
              "Quality Assurance          111\n",
              "Advertising                 90\n",
              "Business Analyst            84\n",
              "Data Analyst                82\n",
              "Public Relations            76\n",
              "Manufacturing               74\n",
              "General Business            68\n",
              "Research                    50\n",
              "Legal                       47\n",
              "Strategy/Planning           46\n",
              "Training                    38\n",
              "Supply Chain                36\n",
              "Financial Analyst           33\n",
              "Distribution                24\n",
              "Purchasing                  15\n",
              "Science                     14\n",
              "Name: function, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# subset job functions that only appear over 100 times\n",
        "function_df = df[df.groupby('function')['function'].transform('size')>200]"
      ],
      "metadata": {
        "id": "RscrHhVxN4Fh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get the mean fradulency in relation to job function (i.e., proportion of fraudulent job postings per job function)\n",
        "func_df = function_df.groupby([function_df['function']]).agg({'fraudulent':['mean']}).reset_index()\n",
        "func_df = func_df.sort_values(by=[('fraudulent', 'mean')], ascending=False)"
      ],
      "metadata": {
        "id": "NX_efW87N6nF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(18,8))\n",
        "plt.margins(x=0.01)\n",
        "plt.bar(func_df['function'], func_df[('fraudulent', 'mean')])\n",
        "plt.xticks([0,1,2,3,4,5,6,7,8,9,10,11,12,13],\n",
        "           ['Administrative', 'Accounting/ \\nAuditing', 'Other','Engineering', 'Business\\nDevelopment',\n",
        "            'Customer\\nService', 'Human\\nResources', 'Sales','Mangement', 'IT', 'Marketing',\n",
        "            'Design','Education', 'Health Care\\nProvider'])\n",
        "\n",
        "plt.ylabel('Mean Proportion Fraudulent',fontsize=18)\n",
        "plt.xlabel('Job Function', fontsize=18)"
      ],
      "metadata": {
        "id": "lQU5vOofN_st",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "outputId": "565e38ff-cb06-4506-b37e-95fd9e5ec03c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Job Function')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x800 with 1 Axes>"
            ],
            "image/png": 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frFmFniRnnnlmHnjggUyfPj0PP/xwDj300DRq1Chf/epXkyRt2rTJcccdlxEjRuT+++/PlClTMnz48PTt2zef/vSnN/g9AwAAAADQ8FS8zPycc87J7bffnrPOOit33HFHXda0isMPPzzz5s3Leeedlzlz5qRnz56ZMGFCzZuXzpgxI1VV//f3gVmzZmWPPfao+fzyyy/P5Zdfnn79+mXSpElJktdeey1f/epX8+abb+YTn/hE9ttvv/zlL3/JJz7xiZrrrrjiilRVVWXo0KFZsmRJBg4cmJ/+9Kcb9F4BAAAAAGi4SkVRFJVePGnSpAwdOjR77rlnzjnnnOy9995p0aJFXda3yVu4cGHatGmTBQsWpHXr1mVf1+3s8Ruwqo+/6ZcOru8SAAAAAIBNSLlZbsUr1Rs1alTz8X333Zf77rtvrdeUSqUsX7680iEBAAAAAKBeVRyqr8cCdwAAAAAA2CRVHKrff//9dVkHAAAAAAA0eBWH6v369avLOgAAAAAAoMGrqu8CAAAAAABgU1FnoXpRFHnjjTcyY8aMuuoSAAAAAAAalPUO1adOnZovfelLadOmTTp27Jju3bvXOv/Pf/4z3/zmN3PiiSfm3XffXd/hAAAAAACg3lS8p3qS3HjjjfnGN76RZcuWrbHN5ptvnmnTpuX+++9P//79c8QRR6zPkAAAAAAAUG8qXqn+97//Pccff3yWLVuWU089NY8//njat2+/2rbDhg1LURT53//934oLBQAAAACA+lbxSvXRo0dn6dKlOfnkkzNmzJgkSaNGjVbb9qCDDkqSTJkypdLhAAAAAACg3lW8Uv3+++9PqVTKWWedtda2nTt3TrNmzfLqq69WOhwAAAAAANS7ikP1WbNmpUWLFtlqq63Kat+8eXNvVAoAAAAAwCat4lC9uro6S5cuTVEUa227ZMmSzJ8/P23btq10OAAAAAAAqHcVh+rdu3fPsmXL8sILL6y17R//+MesWLEiu+66a6XDAQAAAABAvas4VP/c5z6Xoihq3qR0TRYtWpSzzz47pVIp//Ef/1HpcAAAAAAAUO8qDtVPP/30tGnTJtdee23OPffczJ8/v9b5d999N3fccUf23nvvPPfcc+nUqVNOOOGE9a0XAAAAAADqTcWhevv27XPbbbeladOm+cEPfpCOHTvmjTfeSJJ07tw5bdq0yWGHHZbnn38+LVu2zO23354WLVrUWeEAAAAAALCxVRyqJ8mAAQPyl7/8Jf3798+yZcuyYsWKFEWROXPmZPny5SmKIv3798/kyZPTt2/fuqoZAAAAAADqReP17WD33XfPxIkT88orr+Shhx7KrFmzsmLFinTq1Cn77rtvtt9++7qoEwAAAAAA6t16h+of2GabbbLNNtvUVXcAAAAAANDgrNf2LwAAAAAA8O9EqA4AAAAAAGUqa/uXAw88sE4GK5VKmThxYp30BQAAAAAAG1tZofqkSZPqZLBSqVQn/QAAAAAAQH0oK1Q///zzN3QdAAAAAADQ4AnVAQAAAACgTN6oFAAAAAAAyiRUBwAAAACAMgnVAQAAAACgTGXtqb463bt3X+drSqVSpk2bVumQAAAAAABQryoO1adPn15Wu1KplKIoaj4GAAAAAIBNVcWh+vXXX/+R5xcsWJBHHnkkd9xxR1q2bJmLLroozZs3r3Q4AAAAAACodxWH6sOGDSur3bPPPpvPfvaz+dWvfpVJkyZVOhwAAAAAANS7Df5GpTvvvHPGjh2bhx9+OGPGjNnQwwEAAAAAwAZT8Ur1dTF48OA0adIkN954Y84888yNMSTUqW5nj6/vEjZZ0y8dXN8lAAAAAECd2eAr1ZOkcePGqa6uzrRp0zbGcAAAAAAAsEFslFD9hRdeyKJFi7LZZpttjOEAAAAAAGCD2OCh+syZM3PMMcekVCqld+/eG3o4AAAAAADYYCreU/3YY4/9yPPvvfdeXn311Tz22GNZtmxZSqWS/dQBAAAAANikVRyq33DDDSmVSimKYq1tW7ZsmTFjxmTgwIGVDgcAAAAAAPWu4lD96KOPTqlUWnPHjRtn8803T48ePfKFL3whrVu3rnQoAAAAAABoENZrpToAAAAAAPw72eBvVAoAAAAAAB8XQnUAAAAAACiTUB0AAAAAAMpU1p7qjRo1qpPBSqVSli9fXid9AQAAAADAxlZWqF4UxYauAwAAAAAAGryyQvX7779/tcdffvnljBgxIu+8806+/OUv58ADD8xWW22VJJk5c2buu+++3H777WnRokVGjx6dbt261VnhAAAAAACwsZUVqvfr12+VY6+//nqOPPLItGrVKn/605+y6667rtLm2GOPzTnnnJPPfe5z+d73vpepU6euf8UAAAAAAFBPKn6j0ksuuSRz5szJ//zP/6w2UP/ALrvskmuvvTYzZ87M97///UqHAwAAAACAeldxqD5+/Pg0bdo0Bx988FrbHnzwwWnWrFnuuuuuSocDAAAAAIB6V3GoPmvWrDRuXNbuMUmSxo0bZ/bs2ZUOBwAAAAAA9a7iUL1t27Z5++23M2XKlLW2nTJlShYtWpQ2bdpUOhwAAAAAANS7ikP1Aw88MEVR5Pjjj8+bb765xnZvvfVWjj/++JRKpRx44IGVDgcAAAAAAPWu/P1bPuS8887LHXfckb/+9a/Zeeedc9JJJ+WAAw5Ily5dkiQzZ87M/fffn2uuuSbz5s1L06ZNc+6559ZZ4QAAAAAAsLFVHKrvuOOO+e1vf5sjjjgib7zxRi655JJccsklq7QriiKtWrXKr3/96+y0007rVSwAAAAAANSnird/SZJBgwblmWeeyYknnpjNN988RVHUemy++eY58cQT8/TTT+dzn/tcXdUMAAAAAAD1ouKV6h/o0qVLfvrTn+anP/1pXn755bz++utJkg4dOmTbbbdd7wIBAAAAAKChWO9Q/V9tu+22gnQAAAAAAD621mv7FwAAAAAA+HdS8Ur1GTNmVHTd1ltvXemQAAAAAABQryoO1SvZ5qVUKmX58uWVDgkAAAAAAPWq4lC9KIqNcg0AAAAAADQUFYfqL7/88keeX7BgQR555JFcccUVmTdvXm688cbsvPPOlQ4HAAAAAAD1ruJQfZtttllrm0996lP5+te/noMOOijHHXdcnnjiiUqHAwAAAACAele1oQdo2rRprrrqqsyePTvf//73N/RwAAAAAACwwWzwUD1JevXqlRYtWuSuu+6quI+xY8emW7duadq0afr06ZNHH310jW2feeaZDB06NN26dUupVMqYMWNWaTNq1KjstddeadWqVTp06JAhQ4bk+eefr9Wmf//+KZVKtR4nnnhixfcAAAAAAMCmbaOE6itXrsyKFSsye/bsiq6/5ZZbMmLEiJx//vmZOnVqevTokYEDB+b1119fbfvFixene/fuufTSS9OpU6fVtnnggQdy8skn5y9/+UvuueeeLFu2LAcffHDeeeedWu2OP/74zJ49u+bxox/9qKJ7AAAAAABg01fxnurr4v777897772Xjh07VnT96NGjc/zxx2f48OFJkmuuuSbjx4/PuHHjcvbZZ6/Sfq+99spee+2VJKs9nyQTJkyo9fkNN9yQDh06ZMqUKdl///1rjjdv3nyNwTwAAAAAAP9eNuhK9WXLluXWW2/NsGHDUiqVcuCBB65zH0uXLs2UKVMyYMCAmmNVVVUZMGBAJk+eXGe1LliwIEnSrl27Wsd/9atfpX379tltt90ycuTILF68+CP7WbJkSRYuXFjrAQAAAADAx0PFK9W7d+/+keffe++9vP766ymKIkVRpE2bNjn//PPXeZw33ngjK1asWGWVe8eOHfPcc8+tc3+rs3Llypx++unZd999s9tuu9UcP/LII7PNNtukc+fOeeqpp3LWWWfl+eefzx133LHGvkaNGpULL7ywTuoCAAAAAKBhqThUnz59etlt99tvv/zkJz/JJz/5yUqH26BOPvnkPP300/nzn/9c6/gJJ5xQ8/Huu++eLbfcMgcddFCmTZuW7bbbbrV9jRw5MiNGjKj5fOHChenateuGKRwAAAAAgI2q4lD9+uuv/+iOGzfO5ptvnh49eqRLly6VDpP27dunUaNGmTt3bq3jc+fOrZO9zk855ZT84Q9/yIMPPpitttrqI9v26dMnSfLSSy+tMVSvrq5OdXX1etcFAAAAAEDDU3GoPmzYsLqsY42aNGmSXr16ZeLEiRkyZEiS97drmThxYk455ZSK+y2KIt/+9rfz29/+NpMmTcq222671muefPLJJMmWW25Z8bgAAAAAAGy6Kg7VN6YRI0Zk2LBh6d27d/bee++MGTMm77zzToYPH54kOfroo9OlS5eMGjUqyftvbvr3v/+95uOZM2fmySefTMuWLbP99tsneX/Ll5tuuim/+93v0qpVq8yZMydJ0qZNmzRr1izTpk3LTTfdlM997nPZYost8tRTT+WMM87I/vvvn0996lP18FUAAAAAAKC+bRKh+uGHH5558+blvPPOy5w5c9KzZ89MmDCh5s1LZ8yYkaqqqpr2s2bNyh577FHz+eWXX57LL788/fr1y6RJk5IkP/vZz5Ik/fv3rzXW9ddfn2OOOSZNmjTJvffeWxPgd+3aNUOHDs33vve9DXuzAAAAAAA0WHUWqr/++ut57bXX8s4776QoijW223///Svq/5RTTlnjdi8fBOUf6Nat20fWkGSt57t27ZoHHnhgnWoEAAAAAODjbb1D9auvvjpXXXVVpk2btta2pVIpy5cvX98hAQAAAACgXqxXqH7EEUfktttuW+uq7w+U2w4AAAAAABqiqrU3Wb2bb745t956a1q3bp3bb78977zzTpKkU6dOWb58eV577bVcf/312X777dO+fftMnDgxK1eurLPCAQAAAABgY6s4VL/hhhtSKpVy8cUX50tf+lKaNWv2f51WVaVz584ZNmxYpk6dmq5du2bIkCF56aWX6qRoAAAAAACoDxWH6k888USS5Gtf+1qt4x9ejd6yZctcffXVWbRoUX74wx9WOhwAAAAAANS7ikP1+fPnp1WrVmnbtm3Nsc0226xmG5h/1bdv3zRv3jz33ntvpcMBAAAAAEC9qzhU32KLLVIqlWoda9u2bRYvXpz58+ev9po5c+ZUOhwAAAAAANS7ikP1Ll26ZOHChXn77bdrju28885Jkvvvv79W26lTp2bx4sVp3rx5pcMBAAAAAEC9qzhU33PPPZMkjz32WM2xwYMHpyiKnHnmmXnssceybNmyPP744xk2bFhKpVL23Xff9a8YAAAAAADqScWh+gcB+m233VZz7KSTTkqXLl3y8ssv59Of/nSaNm2aPn365Jlnnknjxo3zX//1X3VSNAAAAAAA1IeKQ/XPfe5zuf/++zN8+PCaYy1btsx9992Xvn37piiKmsfWW2+dO+64I3369KmTogEAAAAAoD40rvjCxo3Tr1+/VY7vsMMOeeihh/Laa6/l1VdfTZs2bbLzzjuv8qamAAAAAACwqak4VJ8xY0aSpEOHDmnatOkq57faaqtstdVWlVcGAAAAAAANTMXbv3Tr1i3du3fPW2+9VZf1AAAAAABAg1XxSvWWLVtms802S+fOneuyHgAAAAAAaLDWa6X64sWLs2LFirqsBwAAAAAAGqyKQ/UhQ4Zk6dKlufvuu+uyHgAAAAAAaLAqDtXPOuusbL/99jnxxBPz1FNP1WVNAAAAAADQIFW8p/pvfvObfPOb38wFF1yQ3r17Z9CgQdl3333ToUOHNGrUaI3XHX300ZUOCQAAAAAA9ariUP2YY45JqVRKkhRFkfHjx2f8+PEfeU2pVBKqAwAAAACwyao4VN96661rQnUAAAAAAPh3UHGoPn369DosAwAAAAAAGr6y36j0wQcfzF/+8pcNWQsAAAAAADRoZa9U79+/f7bccsvMnDlzlXPXXXdd3nnnnZx66ql1WhwAAAAAADQkZa9UT95/Q9LV+d73vpczzjijTgoCAAAAAICGap1CdQAAAAAA+HcmVAcAAAAAgDIJ1QEAAAAAoExCdQAAAAAAKJNQHQAAAAAAytR4XRovXbo0f/rTn1IUxSrHk6z23Iftv//+61giAAAAAAA0DOsUqv/zn/9M//7913j+o84lSalUyvLly9dlSAAAAAAAaDDWKVRf2yp0AAAAAAD4OCs7VL///vs3ZB0AAAAAANDglR2q9+vXb0PWAQAAAAAADV5VfRcAAAAAAACbCqE6AAAAAACUSagOAAAAAABlEqoDAAAAAECZhOoAAAAAAFAmoToAAAAAAJRJqA4AAAAAAGUSqgMAAAAAQJmE6gAAAAAAUCahOgAAAAAAlKlxXXX03nvv5Z///GeWLVv2ke223nrruhoSAAAAAAA2qvUK1RcvXpwf/ehH+fWvf52XXnppre1LpVKWL1++PkMCAAAAAEC9qThUnz9/fvbff/8888wzKYqirGvKbQcAAAAAAA1RxaH6xRdfnKeffjqbbbZZvv3tb+eLX/xiOnfunMaN62xHGQAAAAAAaFAqTsDvvPPOlEqljBkzJieddFJd1gQAAAAAAA1SVaUXzpw5M1VVVRk+fHhd1gMAAAAAAA1WxSvV27Vrl/feey9Nmzaty3oAAAAAAKDBqnil+n777ZcFCxZk5syZdVkPAAAAAAA0WBWH6meddVYaN26ciy++uC7rAQAAAACABqviUL1Xr1654YYb8otf/CLHHXdc/vGPf9RlXQAAAAAA0OBUvKd69+7dkySNGjXKDTfckBtuuCHt2rVLq1at1nhNqVTKtGnTKh0SAAAAAADqVcWh+vTp01c59uabb+bNN99c4zWlUqnS4QAAAAAAoN5VHKpff/31dVkHAAAAAAA0eBWH6sOGDavLOgAAAAAAoMGr+I1KAQAAAADg341QHQAAAAAAyrTeoXpRFLnjjjty2GGHZdttt02LFi3SokWLbLvttvnKV76SO++8M0VR1EWtAAAAAABQryreUz1J5s6dmy9/+ct5+OGHk6RWeP7KK69kxowZ+c1vfpN99903t956azp16rR+1QIAAAAAQD2qOFRfunRpBg4cmL/97W8piiJ77713PvvZz2arrbZKkrz22mu5995788gjj+Shhx7KIYcckkcffTSbbbZZnRUPAAAAAAAbU8Wh+s9+9rM89dRTad26dX75y1/m85///CptLr744tx999058sgj89RTT+Waa67Jt7/97fUqGAAAAAAA6kvFe6rfeuutKZVKGTt27GoD9Q987nOfy9ixY1MURW6++eZKhwMAAAAAgHpXcaj+7LPPZrPNNsvhhx++1raHH354mjRpkmeffbbS4QAAAAAAoN5VHKq/++67ad68eRo3XvsOMo0bN07z5s3z7rvvVjpcxo4dm27duqVp06bp06dPHn300TW2feaZZzJ06NB069YtpVIpY8aMqajP9957LyeffHK22GKLtGzZMkOHDs3cuXMrvgcAAAAAADZtFYfqHTt2zIIFCzJjxoy1tp0+fXrmz5+fjh07VjTWLbfckhEjRuT888/P1KlT06NHjwwcODCvv/76atsvXrw43bt3z6WXXppOnTpV3OcZZ5yRu+66K7fddlseeOCBzJo1K1/60pcqugcAAAAAADZ9FYfq+++/f4qiyBlnnJGiKNbYriiKjBgxIqVSKf369atorNGjR+f444/P8OHDs8suu+Saa65J8+bNM27cuNW232uvvXLZZZfliCOOSHV1dUV9LliwINddd11Gjx6dAw88ML169cr111+fhx9+OH/5y18qug8AAAAAADZtFYfqHwTld955Zw488MBMnDgxy5Ytqzm/bNmy3HvvvTnggANy5513plQq5YwzzljncZYuXZopU6ZkwIAB/1d0VVUGDBiQyZMnV1R7OX1OmTIly5Ytq9Vmp512ytZbb/2R4y5ZsiQLFy6s9QAAAAAA4OOh4lC9Z8+eufzyy1MURR588MEcfPDBadmyZbp06ZIuXbqkZcuWGThwYB588MEkyeWXX56ePXuu8zhvvPFGVqxYscrWMR07dsycOXMqqr2cPufMmZMmTZqkbdu26zTuqFGj0qZNm5pH165dK6oRAAAAAICGp+JQPXl/z/Hf//732XHHHVMURZYtW5bZs2dn9uzZWbZsWYqiyC677JK77rorp59+eh2V3LCNHDkyCxYsqHm8+uqr9V0SAAAAAAB1pPH6dvD5z38+n//85/O3v/0tjz/+eM0bfXbo0CG9e/fO7rvvvl79t2/fPo0aNcrcuXNrHZ87d+4a34S0Lvrs1KlTli5dmvnz59darb62caurq9e4jzsAAAAAAJu29Q7VP7D77ruvd4C+Ok2aNEmvXr0yceLEDBkyJEmycuXKTJw4MaeccsoG67NXr17ZbLPNMnHixAwdOjRJ8vzzz2fGjBnp27fvet8XAAAAAACbnjoL1TekESNGZNiwYendu3f23nvvjBkzJu+8806GDx+eJDn66KPTpUuXjBo1Ksn7b0T697//vebjmTNn5sknn0zLli2z/fbbl9VnmzZtctxxx2XEiBFp165dWrdunW9/+9vp27dvPv3pT9fDVwEAAAAAgPq2SYTqhx9+eObNm5fzzjsvc+bMSc+ePTNhwoSaNxqdMWNGqqr+b3v4WbNmZY899qj5/PLLL8/ll1+efv36ZdKkSWX1mSRXXHFFqqqqMnTo0CxZsiQDBw7MT3/6041z08BqdTt7fH2XsMmafung+i4BAAAAYJNXKoqiWFujAw88MEmyzTbb5Prrr691bJ0GK5UyceLEdb5uU7Zw4cK0adMmCxYsSOvWrcu+TnC4fuo6PDQflTMXDYdQHQAAAGDNys1yy1qp/sHq7p122mmVY+uiVCqt8zUAAAAAANBQlBWqn3/++UmS9u3br3IMAAAAAAD+XaxTqL62YwAAAAAA8HFWtfYmAAAAAABAsh6h+kUXXZTRo0eX3f6qq67KRRddVOlwAAAAAABQ7yoO1S+44IJcfvnlZbe/4oorcuGFF1Y6HAAAAAAA1DvbvwAAAAAAQJk2Wqj+1ltvpWnTphtrOAAAAAAAqHMbJVS/7bbbsmjRomy99dYbYzgAAAAAANggGpfb8Morr8yVV15Z69i8efPSvXv3NV5TFEXmz5+fhQsXplQqZfDgwZVXCgAAAAAA9azsUH3+/PmZPn16rWMrVqxY5diaHHTQQTnvvPPWpTYAAAAAAGhQyg7VhwwZkm7duiV5fwX6sccemzZt2mTMmDFrvKaqqiqtW7fObrvtlu222259awUAAAAAgHpVdqjeo0eP9OjRo+bzY489Ns2aNcuwYcM2SGEAAAAAANDQlB2qf9j999+fUqmU+fPnp23btnVYEgAAAAAANEwVh+oHHHBAGjVqlNdff70u6wEAAAAAgAar4lC9TZs2adSoUTbffPO6rAcAAAAAABqsqkov3H777bNo0aIsWbKkLusBAAAAAIAGq+JQ/YgjjsiyZcty66231mU9AAAAAADQYFUcqp922mnp27dvTjnllNx99911WRMAAAAAADRIFe+p/oMf/CD7779//va3v+ULX/hCdt111+y7777p0KFDGjVqtMbrzjvvvEqHBAAAAACAelVxqH7BBRekVCqlKIokydNPP51nnnlmrdcJ1QEAAAAA2FRVHKrvv//+KZVKdVkLAAAAAAA0aBWH6pMmTarDMgAAAAAAoOGr+I1KAQAAAADg341QHQAAAAAAylTx9i//aunSpbnnnnvy+OOP5/XXX0+SdOjQIXvttVcGDBiQJk2a1MUwAAAAAABQr9Y7VL/22mtz7rnn5o033ljt+fbt2+eSSy7J8ccfv75DAQAAAABAvVqvUP2ss87K5ZdfnqIokiRdunTJVlttlSR57bXXMnPmzMybNy8nnnhipk2blksvvXT9KwYAAAAAgHpS8Z7qDzzwQC677LIURZGhQ4fm73//e1599dVMnjw5kydPzquvvppnn302X/7yl1MURS677LL86U9/qsvaAQAAAABgo6o4VB87dmyS5Ljjjsttt92WnXbaaZU2O+64Y2699dYcd9xxKYoiV199deWVAgAAAABAPas4VH/44YdTVVWV73//+2tte8kll6RUKuWhhx6qdDgAAAAAAKh3FYfqb7zxRtq0aZMOHTqstW3Hjh3Ttm3bNb6ZKQAAAAAAbAoqDtVbtWqVRYsW5b333ltr23fffTeLFi1Ky5YtKx0OAAAAAADqXcWh+qc+9amsWLEi48aNW2vbcePGZfny5enRo0elwwEAAAAAQL2rOFQ/6qijUhRFvvOd7+S6665bY7uf//zn+c53vpNSqZSvf/3rlQ4HAAAAAAD1rnGlFx5zzDG58cYb88ADD+SEE07IRRddlAMOOCBdunRJkrz22mu5//77M3PmzBRFkf79+2fYsGF1VjgAAAAAAGxsFYfqVVVV+d3vfpdjjz02d9xxR1599dXceOONtdoURZEkGTp0aK677rqUSqX1qxYAAAAAAOpRxaF6krRu3Tq33357Hn300dxyyy15/PHH8/rrrydJOnTokN69e+eII47IXnvtVSfFAgAAAABAfVqvUP0De++9d/bee++66AoAAAAAABqsit+oFAAAAAAA/t3UyUr1JFm0aFGmTp1aa/uXPfbYI61bt66rIQAAAAAAoF6td6j+5JNP5txzz82ECROycuXKWueqqqoyaNCgXHTRRdljjz3WdygAAAAAAKhX67X9y/XXX58+ffrk7rvvzooVK1IURa3HihUrMn78+PTp0yfjxo2rq5oBAAAAAKBeVLxS/dFHH83xxx+flStXZocddsh3v/vdHHjggenSpUuSZObMmbnvvvsyevToPPfccznhhBOy2267eUNTgI+JbmePr+8SNlnTLx1c3yUAAAAAFap4pfqoUaOycuXK9O/fP3/961/zjW98I927d091dXWqq6vTvXv3fOMb38gTTzyRAw44ICtXrsyoUaPqsnYAAAAAANioKg7V//znP6dUKuVnP/tZmjZtusZ21dXVGTt2bM01AAAAAACwqao4VH/nnXfSunXr7Ljjjmttu9NOO6VNmzZZvHhxpcMBAAAAAEC9qzhU33rrrfPee+9l5cqVa227YsWKvPfee+natWulwwEAAAAAQL2rOFQ/9NBDs3Tp0tx5551rbXvnnXdmyZIlGTp0aKXDAQAAAABAvas4VP+v//qv7LDDDjnhhBMyadKkNbZ78MEH881vfjM77bRTRo4cWelwAAAAAABQ7xpXeuEdd9yRE088MRdeeGEOOuig7LvvvjnwwAPTpUuXJMnMmTNz//33589//nPatGmTb37zm7njjjtW29fRRx9daRkAAAAAALDRVByqH3PMMSmVSkmSoijy0EMP5aGHHlqlXVEUWbBgQUaMGLHafkqlklAdAAAAAIBNQsWh+tZbb10TqgMAAAAAwL+DikP16dOn12EZAAAAAADQ8FX8RqUAAAAAAPDvRqgOAAAAAABlqnj7lw975pln8vjjj+f1119PknTo0CF77bVXdtlll7oaAgAAAAAA6tV6h+p//OMf85//+Z95+umnV3t+9913z49+9KMcfPDB6zsUAAAAAADUq/Xa/uXqq6/O4MGD8/TTT6coilRVVaVDhw7p0KFDGjVqlKIo8tRTT+WQQw7J2LFj66pmAAAAAACoFxWH6n/9619z+umnZ+XKldl7771z99135+23387s2bMze/bsLFq0KHfffXf69u2boihy+umn56mnnqrL2gEAAAAAYKOqOFQfPXp0Vq5cmS984Qv585//nEGDBqW6urrmfHV1dQYNGpQHH3wwX/jCF7JixYpcccUVdVI0AAAAAADUh4pD9QceeCClUilXXnllGjVqtMZ2jRo1ypgxY5Ik999/f6XDAQAAAABAvas4VJ87d27atGmTbt26rbXttttum7Zt22bu3LmVDgcAAAAAAPWu4lC9WbNmWbx4cZYvX77WtsuXL8/ixYvTrFmzSocDAAAAAIB6V3GovvPOO2fZsmW5/fbb19r2tttuy9KlS7PzzjtXOhwAAAAAANS7ikP1ww47LEVR5Fvf+lYmTpy4xnb33ntvvvWtb6VUKuUrX/lKpcMBAAAAAEC9a1zphSeddFKuu+66PPPMMzn44IPTt2/fDBgwIF26dEmSvPbaa5k4cWImT56coiiy22675aSTTqqzwgEAAAAAYGOreKV6dXV1/vjHP2bvvfdOURR5+OGHc/HFF+fEE0/MiSeemEsuuSQPP/xwiqJInz59MmHChDRp0mS9ih07dmy6deuWpk2bpk+fPnn00Uc/sv1tt92WnXbaKU2bNs3uu++eu+++u9b5Uqm02sdll11W06Zbt26rnL/00kvX6z4AAAAAANg0VRyqJ0nnzp3z8MMP5+abb86hhx6arbbaKk2aNEmTJk2y1VZb5dBDD80tt9yShx56KJ07d16vQm+55ZaMGDEi559/fqZOnZoePXpk4MCBef3111fb/uGHH85Xv/rVHHfccXniiScyZMiQDBkyJE8//XRNm9mzZ9d6jBs3LqVSKUOHDq3V10UXXVSr3be//e31uhcAAAAAADZNFW//8oGqqqp85Stf2eD7pY8ePTrHH398hg8fniS55pprMn78+IwbNy5nn332Ku2vvPLKDBo0KN/97neTJBdffHHuueeeXH311bnmmmuSJJ06dap1ze9+97sccMAB6d69e63jrVq1WqUtAAAAAAD/fipeqV5VVZXGjRvnpZdeqst6Vmvp0qWZMmVKBgwYUGv8AQMGZPLkyau9ZvLkybXaJ8nAgQPX2H7u3LkZP358jjvuuFXOXXrppdliiy2yxx575LLLLsvy5cvXWOuSJUuycOHCWg8AAAAAAD4eKl6p3qxZs2y22WbZfvvt67Ke1XrjjTeyYsWKdOzYsdbxjh075rnnnlvtNXPmzFlt+zlz5qy2/S9+8Yu0atUqX/rSl2odP/XUU7PnnnumXbt2efjhhzNy5MjMnj07o0ePXm0/o0aNyoUXXljurQEAAAAAsAmpOFTfaqut8tprr9VlLfVq3LhxOeqoo9K0adNax0eMGFHz8ac+9ak0adIk3/zmNzNq1KhUV1ev0s/IkSNrXbNw4cJ07dp1wxUOAAAAAMBGU/H2L4MHD857772XBx54oC7rWa327dunUaNGmTt3bq3jc+fOXeNe5506dSq7/Z/+9Kc8//zz+cY3vrHWWvr06ZPly5dn+vTpqz1fXV2d1q1b13oAAAAAAPDxUHGoPnLkyHziE5/ISSedlNmzZ9dlTato0qRJevXqlYkTJ9YcW7lyZSZOnJi+ffuu9pq+ffvWap8k99xzz2rbX3fddenVq1d69Oix1lqefPLJVFVVpUOHDut4FwAAAAAAbOoq3v7l2Wefzfe///2cccYZ2WWXXfL1r389++67bzp06JBGjRqt8br999+/ovFGjBiRYcOGpXfv3tl7770zZsyYvPPOOxk+fHiS5Oijj06XLl0yatSoJMlpp52Wfv365cc//nEGDx6cm2++OY8//niuvfbaWv0uXLgwt912W3784x+vMubkyZPzyCOP5IADDkirVq0yefLknHHGGfna176WzTffvKL7AAAAAABg01VxqN6/f/+USqWaz8eOHZuxY8d+5DWlUinLly+vaLzDDz888+bNy3nnnZc5c+akZ8+emTBhQs2bkc6YMSNVVf+38H6fffbJTTfdlO9973s555xzssMOO+TOO+/MbrvtVqvfm2++OUVR5Ktf/eoqY1ZXV+fmm2/OBRdckCVLlmTbbbfNGWecUWvPdAAAAAAA/n2UiqIoKrnwXwPsdbFy5cqKrttULVy4MG3atMmCBQvWaX/1bmeP34BVffxNv3RwnfZnPipnLhoOc9Fw1PVcAAAAAOuv3Cy34pXq/27hOAAAAAAAVPxGpQAAAAAA8O+mopXqr7/+eh5//PEsXLgw7dq1y1577eWNOwEAAAAA+Nhbp1B9wYIFOeGEE/Kb3/wm/7oVe1VVVY455phcddVVadasWZ0XCQAAAAAADUHZofry5cvz2c9+NlOmTMmH39t0xYoVGTduXF599dVMmDChzosEAAAAAICGoOw91X/xi1/k8ccfT1EU+cxnPpP//u//zvjx4zN27Nj06NEjRVHknnvuyV133bUh6wUAAAAAgHpT9kr1W2+9NaVSKUceeWRuvPHGWudOOOGEHHLIIZk4cWJuv/32fOELX6jzQgEAAAAAoL6VvVL9qaeeSpJccsklq5xr1KhRLrroohRFUdMOAAAAAAA+bsoO1d966620aNEi22yzzWrP77777jXtAAAAAADg46jsUH3ZsmVp2bLlGs+3aNGiph0AAAAAAHwclR2qAwAAAADAv7uy36g0SVasWJFXX301RVFU3GbrrbdetwoBAAAAAKCBWKdQ/Y033ki3bt3WeL5UKn1km1KplOXLl6/LkAAAAAAA0GCsU6j+USvUAQAAAADg467sUP3666/fkHUAAAAAAECDV3aoPmzYsA1ZBwAAAAAANHhV9V0AAAAAAABsKoTqAAAAAABQJqE6AAAAAACUSagOAAAAAABlEqoDAAAAAECZhOoAAAAAAFAmoToAAAAAAJRJqA4AAAAAAGUSqgMAAAAAQJmE6gAAAAAAUCahOgAAAAAAlGm9QvWiKHLDDTdk4MCB2XLLLVNdXZ1GjRqt8dG4ceO6qhsAAAAAADa6ilPuJUuWZPDgwbn//vtTFEVd1gQAAAAAAA1SxaH6D3/4w9x3331Jki996Uv54he/mM6dO1uNDgAAAADAx1bFCfjNN9+cUqmU8847L+eff35d1gQAAAAAAA1SxXuqv/zyyymVSvnOd75Tl/UAAAAAAECDVfFK9VatWmXFihVp2bJlXdYDAAAAAAANVsUr1ffaa68sWLAgb731Vl3WAwAAAAAADVbFofqIESNSFEWuuOKKuqwHAAAAAAAarIq3fznooIPywx/+MCNHjkyTJk3yne98J82bN6/L2gAAAAAAoEGpOFQ/8MADk7y/t/oFF1yQUaNGZdddd02rVq3WeE2pVMrEiRMrHRIAAAAAAOpVxaH6pEmTan3+3nvvZcqUKR95TalUqnQ4AAAAAACodxWH6ueff35d1gEAAAAAAA2eUB0AAAAAAMpUVd8FAAAAAADApkKoDgAAAAAAZRKqAwAAAABAmdY7VP/rX/+aE044Ibvssktat26dRo0arfHRuHHFW7gDAAAAAEC9W6+U++qrr86IESOyYsWKFEVRVzUBAAAAAECDVPFK9UceeSSnnXZaVqxYkW9961u5++67kyTt2rXLvffem1/+8pc55phj0qRJk7Rv3z433XRT7rvvvjorHAAAAAAANraKV6pfddVVKYoip59+ekaPHl1zvEmTJjnwwAOTJEceeWROPfXUDBw4MOeee26mTp26/hUDAAAAAEA9qXil+kMPPZRSqZTTTjut1vEPbwPTs2fP/OQnP8m0adNy2WWXVTocAAAAAADUu4pXqs+dOzfV1dXZZpttao5VVVXlvffeW6XtoYcems022yx33HFHLrrookqHBABWo9vZ4+u7hE3W9EsH13cJAAAAbGIqDtWbN2+eUqlU61irVq2ycOHCLFmyJNXV1TXHN9tsszRv3jyvvPJK5ZUCAAAAAEA9q3j7ly5dumThwoVZvnx5zbHtttsuSfLYY4/Vajtr1qwsWLBgla1hAAAAAABgU1JxqL7zzjtnxYoV+dvf/lZzrH///imKIhdddFHNNjBLly7NqaeemiTZfffd17NcAAAAAACoPxWH6gcffHCKoshdd91Vc+zkk09OdXV1Jk6cmK222ir77rtvunTpkt/+9rcplUo55ZRT6qRoAAAAAACoDxXvqT506NC89tpr6dy5c82xbbfdNjfddFOGDx+et956K5MnT07y/huYfve7381RRx21/hUDAAAAAEA9qThUb9u2bc4///xVjh966KHp169f7r777rz66qtp06ZNDj744Gy//fbrVSgAAAAAANS3ikP1j9KuXbt87Wtf2xBdAwAAAABAvdkgoToAwL+jbmePr+8SNlnTLx1c3yUAAACUpU5C9aeeeip//OMf88orr+Tdd9/NddddV3Nu2bJlmTdvXkqlUrbccsu6GA4AAAAAAOrFeoXqCxYsyLHHHps777wzSVIURUql0iqheo8ePfLPf/4zf/3rX7PrrruuV8EAAAAAAFBfqiq9cNmyZTnkkENy5513pnnz5hk8eHCaNm26SrvmzZtn+PDhWblyZW6//fb1KhYAAAAAAOpTxaH6ddddl7/85S/p3r17nn/++fz+979PmzZtVtt26NChSZIHH3yw0uEAAAAAAKDeVRyq//rXv06pVMoVV1yRzp07f2TbPfbYI1VVVXnuuecqHQ4AAAAAAOpdxaH63/72t5RKpRx88MFrbdukSZO0adMmb775ZqXDAQAAAABAvas4VF+8eHFatWqVJk2alNV+2bJladx4vd4XFQAAAAAA6lXFoXr79u2zcOHCvP3222tt+/LLL+ftt99e6zYxAAAAAADQkFUcqvfp0ydJMn78+LW2/clPfpIk+cxnPlPpcAAAAAAAUO8qDtWPPfbYFEWRc889N7NmzVpju//+7//OlVdemVKplBNOOKHS4ZIkY8eOTbdu3dK0adP06dMnjz766Ee2v+2227LTTjuladOm2X333XP33XfXOn/MMcekVCrVegwaNKhWm7feeitHHXVUWrdunbZt2+a4444ra3U+AAAAAAAfPxWH6oMHD87QoUPz0ksvpXfv3jnzzDPz7rvvJkmuvfba/Nd//Vd69OiRb33rWymKIt/4xjdqVrdX4pZbbsmIESNy/vnnZ+rUqenRo0cGDhyY119/fbXtH3744Xz1q1/NcccdlyeeeCJDhgzJkCFD8vTTT9dqN2jQoMyePbvm8etf/7rW+aOOOirPPPNM7rnnnvzhD3/Igw8+uN5/HAAAAAAAYNO0Xu8ceuONN6Zp06b51a9+lSuuuKLm+EknnZQkKYoiyfur2seOHbs+Q2X06NE5/vjjM3z48CTJNddck/Hjx2fcuHE5++yzV2l/5ZVXZtCgQfnud7+bJLn44otzzz335Oqrr84111xT0666ujqdOnVa7ZjPPvtsJkyYkMceeyy9e/dO8v5WNp/73Ody+eWX2yMeAAAAAODfTMUr1ZOkadOmufHGG/Pggw/m61//erbbbrs0a9YsTZo0ydZbb50jjzwykyZNys9//vM0blx5fr906dJMmTIlAwYM+L/Cq6oyYMCATJ48ebXXTJ48uVb7JBk4cOAq7SdNmpQOHTpkxx13zEknnZQ333yzVh9t27atCdSTZMCAAamqqsojjzyy2nGXLFmShQsX1noAAAAAAPDxsF4r1T+w3377Zb/99quLrlbrjTfeyIoVK9KxY8daxzt27JjnnntutdfMmTNnte3nzJlT8/mgQYPypS99Kdtuu22mTZuWc845J4ccckgmT56cRo0aZc6cOenQoUOtPho3bpx27drV6udfjRo1KhdeeGEltwkAAAAAQANXJ6H6puqII46o+Xj33XfPpz71qWy33XaZNGlSDjrooIr6HDlyZEaMGFHz+cKFC9O1a9f1rhUAAAAAgPq3Xtu/bCzt27dPo0aNMnfu3FrH586du8b90Dt16rRO7ZOke/fuad++fV566aWaPj78RqjLly/PW2+9tcZ+qqur07p161oPAAAAAAA+HspeqT5jxow6GXDrrbde52uaNGmSXr16ZeLEiRkyZEiSZOXKlZk4cWJOOeWU1V7Tt2/fTJw4MaeffnrNsXvuuSd9+/Zd4zivvfZa3nzzzWy55ZY1fcyfPz9TpkxJr169kiT33XdfVq5cmT59+qzzfQAAAAAAsGkrO1Tv1q1bSqXSeg1WKpWyfPnyiq4dMWJEhg0blt69e2fvvffOmDFj8s4772T48OFJkqOPPjpdunTJqFGjkiSnnXZa+vXrlx//+McZPHhwbr755jz++OO59tprkyRvv/12LrzwwgwdOjSdOnXKtGnT8p//+Z/ZfvvtM3DgwCTJzjvvnEGDBuX444/PNddck2XLluWUU07JEUcckc6dO6/X1wIAAAAAgE3POu2pXhTFhqpjrQ4//PDMmzcv5513XubMmZOePXtmwoQJNW9GOmPGjFRV/d9uNvvss09uuummfO9738s555yTHXbYIXfeeWd22223JEmjRo3y1FNP5Re/+EXmz5+fzp075+CDD87FF1+c6urqmn5+9atf5ZRTTslBBx2UqqqqDB06NFddddXGvXkAAAAAABqEdQrVS6VSunXrlmOOOSb777//hqppjU455ZQ1bvcyadKkVY4ddthhOeyww1bbvlmzZvnjH/+41jHbtWuXm266aZ3qBAAAAADg46nsUP2QQw7J//t//y8vv/xyLrjggnTv3j3Dhw/PsGHD0qVLlw1ZIwAAAAAANAhVa2/yvvHjx2fGjBn5wQ9+kB122CHTpk3Lueeem27duuWQQw7JbbfdlqVLl27IWgEAAAAAoF6VHaonyZZbbpmzzz47zz33XP70pz/lmGOOqdlG5YM37zz11FMzderUDVUvAAAAAADUm3UK1f/Vvvvum+uuuy5z5szJddddl3333TdvvfVWrr766uy1117p0aNHrrrqqrz55pt1WS8AAAAAANSbikP1DzRv3jzDhw/Pgw8+mBdffDEjR45Mly5d8re//S1nnHFGfvjDH9ZFnQAAAAAAUO/WO1T/V9ttt12OPfbYHHnkkWnSpElddg0AAAAAAPWucV10snjx4tx6660ZN25cHnrooSRJURTZfffdc9BBB9XFEAAAAAAAUO/WK1T/85//nHHjxuX222/PO++8k6Iosvnmm+erX/1qhg8fnl69etVVnQAAAAAAUO/WOVSfNWtWbrjhhtxwww2ZNm1aiqJIVVVVPvvZz2b48OE59NBDbf0CAAAAAMDHUtmh+q233prrr78+9957b1auXJmiKLLddtvlmGOOybBhw7LVVlttyDoBAAAAAKDelR2qH3HEESmVSmnevHkOO+ywDB8+PJ/5zGc2ZG0AAAAAANCgrPP2L82bN8+kSZMyadKkdR6sVCpl2rRp63wdAAAAAAA0BOsUqhdFkXnz5mXevHkVDVYqlSq6DgAA1kW3s8fXdwmbrOmXDq7vEgAAoEErO1Q///zzN2QdAAAAAADQ4AnVAQAAAACgTFX1XQAAAAAAAGwqhOoAAAAAAFAmoToAAAAAAJRJqA4AAAAAAGUSqgMAAAAAQJmE6gAAAAAAUCahOgAAAAAAlEmoDgAAAAAAZRKqAwAAAABAmYTqAAAAAABQJqE6AAAAAACUSagOAAAAAABlEqoDAAAAAECZhOoAAAAAAFAmoToAAAAAAJRJqA4AAAAAAGUSqgMAAAAAQJmE6gAAAAAAUCahOgAAAAAAlEmoDgAAAAAAZRKqAwAAAABAmYTqAAAAAABQJqE6AAAAAACUSagOAAAAAABlEqoDAAAAAECZhOoAAAAAAFAmoToAAAAAAJRJqA4AAAAAAGUSqgMAAAAAQJmE6gAAAAAAUCahOgAAAAAAlEmoDgAAAAAAZRKqAwAAAABAmYTqAAAAAABQJqE6AAAAAACUSagOAAAAAABlEqoDAAAAAECZhOoAAAAAAFAmoToAAAAAAJRJqA4AAAAAAGUSqgMAAAAAQJmE6gAAAAAAUCahOgAAAAAAlEmoDgAAAAAAZRKqAwAAAABAmYTqAAAAAABQJqE6AAAAAACUqXF9FwAAAHx8dTt7fH2XsMmafung+i4BAIDVsFIdAAAAAADKJFQHAAAAAIAyCdUBAAAAAKBMQnUAAAAAACjTJhWqjx07Nt26dUvTpk3Tp0+fPProox/Z/rbbbstOO+2Upk2bZvfdd8/dd99dc27ZsmU566yzsvvuu6dFixbp3Llzjj766MyaNatWH926dUupVKr1uPTSSzfI/QEAAAAA0LBtMqH6LbfckhEjRuT888/P1KlT06NHjwwcODCvv/76ats//PDD+epXv5rjjjsuTzzxRIYMGZIhQ4bk6aefTpIsXrw4U6dOzbnnnpupU6fmjjvuyPPPP5//+I//WKWviy66KLNnz655fPvb396g9woAAAAAQMO0yYTqo0ePzvHHH5/hw4dnl112yTXXXJPmzZtn3Lhxq21/5ZVXZtCgQfnud7+bnXfeORdffHH23HPPXH311UmSNm3a5J577slXvvKV7Ljjjvn0pz+dq6++OlOmTMmMGTNq9dWqVat06tSp5tGiRYsNfr8AAAAAADQ8m0SovnTp0kyZMiUDBgyoOVZVVZUBAwZk8uTJq71m8uTJtdonycCBA9fYPkkWLFiQUqmUtm3b1jp+6aWXZosttsgee+yRyy67LMuXL19jH0uWLMnChQtrPQAAAAAA+HhoXN8FlOONN97IihUr0rFjx1rHO3bsmOeee26118yZM2e17efMmbPa9u+9917OOuusfPWrX03r1q1rjp966qnZc889065duzz88MMZOXJkZs+endGjR6+2n1GjRuXCCy9cl9sDAAAAAGATsUmE6hvasmXL8pWvfCVFUeRnP/tZrXMjRoyo+fhTn/pUmjRpkm9+85sZNWpUqqurV+lr5MiRta5ZuHBhunbtuuGKBwAAAABgo9kkQvX27dunUaNGmTt3bq3jc+fOTadOnVZ7TadOncpq/0Gg/sorr+S+++6rtUp9dfr06ZPly5dn+vTp2XHHHVc5X11dvdqwHQAAAACATd8msad6kyZN0qtXr0ycOLHm2MqVKzNx4sT07dt3tdf07du3Vvskueeee2q1/yBQf/HFF3Pvvfdmiy22WGstTz75ZKqqqtKhQ4cK7wYAAAAAgE3VJrFSPXl/G5Zhw4ald+/e2XvvvTNmzJi88847GT58eJLk6KOPTpcuXTJq1KgkyWmnnZZ+/frlxz/+cQYPHpybb745jz/+eK699tok7wfqX/7ylzN16tT84Q9/yIoVK2r2W2/Xrl2aNGmSyZMn55FHHskBBxyQVq1aZfLkyTnjjDPyta99LZtvvnn9fCEAAAAAAKg3m0yofvjhh2fevHk577zzMmfOnPTs2TMTJkyoeTPSGTNmpKrq/xbe77PPPrnpppvyve99L+ecc0522GGH3Hnnndltt92SJDNnzszvf//7JEnPnj1rjXX//fenf//+qa6uzs0335wLLrggS5Ysybbbbpszzjij1p7pAAAAAAD8+9hkQvUkOeWUU3LKKaes9tykSZNWOXbYYYflsMMOW237bt26pSiKjxxvzz33zF/+8pd1rhMAAAAAgI+nTWJPdQAAAAAAaAiE6gAAAAAAUCahOgAAAAAAlEmoDgAAAAAAZRKqAwAAAABAmYTqAAAAAABQJqE6AAAAAACUSagOAAAAAABlEqoDAAAAAECZhOoAAAAAAFAmoToAAAAAAJRJqA4AAAAAAGVqXN8FAAAAsOF1O3t8fZewSZt+6eD6LgEAaCCsVAcAAAAAgDJZqQ4AAAAbkX81sH78qwEA6puV6gAAAAAAUCahOgAAAAAAlEmoDgAAAAAAZRKqAwAAAABAmYTqAAAAAABQJqE6AAAAAACUSagOAAAAAABlEqoDAAAAAECZhOoAAAAAAFAmoToAAAAAAJRJqA4AAAAAAGUSqgMAAAAAQJmE6gAAAAAAUCahOgAAAAAAlEmoDgAAAAAAZRKqAwAAAABAmYTqAAAAAABQJqE6AAAAAACUqXF9FwAAAABQX7qdPb6+S9hkTb90cH2XAFAvrFQHAAAAAIAyCdUBAAAAAKBMQnUAAAAAACiTUB0AAAAAAMokVAcAAAAAgDIJ1QEAAAAAoExCdQAAAAAAKJNQHQAAAAAAyiRUBwAAAACAMjWu7wIAAAAAoNvZ4+u7hE3W9EsH13cJ8G/FSnUAAAAAACiTUB0AAAAAAMokVAcAAAAAgDIJ1QEAAAAAoExCdQAAAAAAKJNQHQAAAAAAyiRUBwAAAACAMgnVAQAAAACgTEJ1AAAAAAAok1AdAAAAAADKJFQHAAAAAIAyCdUBAAAAAKBMQnUAAAAAACiTUB0AAAAAAMokVAcAAAAAgDIJ1QEAAAAAoExCdQAAAAAAKFPj+i4AAAAAAGg4up09vr5L2GRNv3RwnfZnLipX13Pxr6xUBwAAAACAMgnVAQAAAACgTEJ1AAAAAAAok1AdAAAAAADKJFQHAAAAAIAybVKh+tixY9OtW7c0bdo0ffr0yaOPPvqR7W+77bbstNNOadq0aXbffffcfffdtc4XRZHzzjsvW265ZZo1a5YBAwbkxRdfrNXmrbfeylFHHZXWrVunbdu2Oe644/L222/X+b0BAAAAANDwbTKh+i233JIRI0bk/PPPz9SpU9OjR48MHDgwr7/++mrbP/zww/nqV7+a4447Lk888USGDBmSIUOG5Omnn65p86Mf/ShXXXVVrrnmmjzyyCNp0aJFBg4cmPfee6+mzVFHHZVnnnkm99xzT/7whz/kwQcfzAknnLDB7xcAAAAAgIZnkwnVR48eneOPPz7Dhw/PLrvskmuuuSbNmzfPuHHjVtv+yiuvzKBBg/Ld7343O++8cy6++OLsueeeufrqq5O8v0p9zJgx+d73vpcvfvGL+dSnPpX/7//7/zJr1qzceeedSZJnn302EyZMyM9//vP06dMn++23X37yk5/k5ptvzqxZszbWrQMAAAAA0EA0ru8CyrF06dJMmTIlI0eOrDlWVVWVAQMGZPLkyau9ZvLkyRkxYkStYwMHDqwJzF9++eXMmTMnAwYMqDnfpk2b9OnTJ5MnT84RRxyRyZMnp23btundu3dNmwEDBqSqqiqPPPJIDj300FXGXbJkSZYsWVLz+YIFC5IkCxcuXKd7Xrlk8Tq1p7Z1/XqvjfmonLloOMxFw2EuGg5z0XCYi4bDXDQc5qJhqcv5MBfrx/dGw2EuGg5z0XCYi4ajkrn44JqiKD6y3SYRqr/xxhtZsWJFOnbsWOt4x44d89xzz632mjlz5qy2/Zw5c2rOf3Dso9p06NCh1vnGjRunXbt2NW0+bNSoUbnwwgtXOd61a9c13R4bQJsx9V0BHzAXDYe5aDjMRcNhLhoOc9FwmIuGw1w0LOaj4TAXDYe5aDjMRcNhLhqO9ZmLRYsWpU2bNms8v0mE6puSkSNH1lohv3Llyrz11lvZYostUiqV6rGyurNw4cJ07do1r776alq3bl3f5fxbMxcNh7loOMxFw2I+Gg5z0XCYi4bDXDQc5qLhMBcNh7loOMxFw2EuGo6P61wURZFFixalc+fOH9lukwjV27dvn0aNGmXu3Lm1js+dOzedOnVa7TWdOnX6yPYf/Hfu3LnZcssta7Xp2bNnTZsPvxHq8uXL89Zbb61x3Orq6lRXV9c61rZt24++wU1U69atP1bfNJsyc9FwmIuGw1w0LOaj4TAXDYe5aDjMRcNhLhoOc9FwmIuGw1w0HOai4fg4zsVHrVD/wCbxRqVNmjRJr169MnHixJpjK1euzMSJE9O3b9/VXtO3b99a7ZPknnvuqWm/7bbbplOnTrXaLFy4MI888khNm759+2b+/PmZMmVKTZv77rsvK1euTJ8+fers/gAAAAAA2DRsEivVk2TEiBEZNmxYevfunb333jtjxozJO++8k+HDhydJjj766HTp0iWjRo1Kkpx22mnp169ffvzjH2fw4MG5+eab8/jjj+faa69NkpRKpZx++um55JJLssMOO2TbbbfNueeem86dO2fIkCFJkp133jmDBg3K8ccfn2uuuSbLli3LKaeckiOOOGKt/wQAAAAAAICPn00mVD/88MMzb968nHfeeZkzZ0569uyZCRMm1LzR6IwZM1JV9X8L7/fZZ5/cdNNN+d73vpdzzjknO+ywQ+68887stttuNW3+8z//M++8805OOOGEzJ8/P/vtt18mTJiQpk2b1rT51a9+lVNOOSUHHXRQqqqqMnTo0Fx11VUb78YboOrq6px//vmrbHPDxmcuGg5z0XCYi4bFfDQc5qLhMBcNh7loOMxFw2EuGg5z0XCYi4bDXDQc/+5zUSqKoqjvIgAAAAAAYFOwSeypDgAAAAAADYFQHQAAAAAAyiRUBwAAAACAMgnVNxEXXHBBevbsuV59TJ8+PaVSKU8++eRGHbcuVXIPrF7//v1z+umn13cZm7wbbrghbdu2re8yPpYa2te2VCrlzjvvrO8yGrRJkyalVCpl/vz59V0KQFka2u8a4N/Phvo55PXehuX3x7ppKK+ljjnmmAwZMqS+y2iwyn09161bt4wZM2aj1NSQCdXr0eTJk9OoUaMMHjx4o4zXtWvXzJ49O7vttlvZ15x55pmZOHFiWW3rOoBf3Q+7Su5hfW3seapra/qheMcdd+Tiiy/eIGO+8soradasWd5+++0N0v+G8Oqrr+bYY49N586d06RJk2yzzTY57bTT8uabb9a08Yvj/xxzzDEplUqrPAYNGlRnYxx++OF54YUX6qy/9TV79uwccsgh9V3GevnwvG2xxRYZNGhQnnrqqTrpf5999sns2bPTpk2bOunv42rOnDn59re/ne7du6e6ujpdu3bNF77whbJ/334Uf3yuO2t60eWPRw3LvHnzctJJJ2XrrbdOdXV1OnXqlIEDB+ahhx6q79IapA9+D5x44omrnDv55JNTKpVyzDHHbPzCGqh/l+/3D37ere653b8+LrjggvoudaPbFL5n6uP1XkP3r895N9tss3Ts2DGf/exnM27cuKxcubJOxmhor1U2po3xWnB9rek58ZVXXpkbbrihXmqqVH0+J63rPx5tyNdB9UGoXo+uu+66fPvb386DDz6YWbNmbfDxGjVqlE6dOqVx48ZlX9OyZctsscUWdVrHsmXLKr62kntYXxt7njaWdu3apVWrVhuk79/97nc54IAD0rJlyw3Sf137xz/+kd69e+fFF1/Mr3/967z00ku55pprMnHixPTt2zdvvfXWRq9pfb5PNpZBgwZl9uzZtR6//vWv66z/Zs2apUOHDnXWX6WWLl2aJOnUqVOqq6vruZr196/zNnHixDRu3Dif//zn66TvJk2apFOnTimVSnXS38fR9OnT06tXr9x333257LLL8re//S0TJkzIAQcckJNPPrm+y9soNoWfb2w6hg4dmieeeCK/+MUv8sILL+T3v/99+vfvX+uP4tTWtWvX3HzzzXn33Xdrjr333nu56aabsvXWW9djZdS3f31ON2bMmLRu3brWsTPPPLO+S6wXG/J7ZkP+TtyQr/c2BR88550+fXr+93//NwcccEBOO+20fP7zn8/y5cvXu/+G8lqlvmzo14IbSps2bfwLg3qyoV4HrVixos7+WLauhOr15O23384tt9ySk046KYMHD17lL2WXXnppOnbsmFatWuW4447Le++9V+v8B3+p+sEPfpCOHTumbdu2ueiii7J8+fJ897vfTbt27bLVVlvl+uuvr7nmw3+p++CvWhMnTkzv3r3TvHnz7LPPPnn++edrrvnw6vNJkyZl7733TosWLdK2bdvsu+++eeWVV3LDDTfkwgsvzF//+teav1J+cE+lUik/+9nP8h//8R9p0aJFvv/972fFihU57rjjsu2226ZZs2bZcccdc+WVV9Ya9xe/+EV+97vf1fQ3adKkWvewcuXKbLXVVvnZz35W62vzxBNPpKqqKq+88kqSZP78+fnGN76RT3ziE2ndunUOPPDA/PWvf62TeUqSu+66K3vttVeaNm2a9u3b59BDD605t2TJkpx11lnp2rVrqqurs/322+e6666rOf/AAw9k7733TnV1dbbccsucffbZtX7Br25ldM+ePWutEimVSvn5z3+eQw89NM2bN88OO+yQ3//+90nen/MDDjggSbL55pvXWknx4X8O2K1bt/zgBz/Isccem1atWmXrrbfOtddeW2vshx9+OD179kzTpk3Tu3fv3Hnnnav96+/vfve7/Md//MfavrwNxsknn5wmTZrk//2//5d+/fpl6623ziGHHJJ77703M2fOzH/913+lf//+eeWVV3LGGWfU/D/5r/74xz9m5513TsuWLWueYPyrn//859l5553TtGnT7LTTTvnpT39ac+6D/69vueWW9OvXL02bNs2vfvWrjXLv6+ODFYH/+th8882TfPT/lx/4/e9/nx122CFNmzbNAQcckF/84he1/tL+4b+Kf/Dz6MYbb0y3bt3Spk2bHHHEEVm0aFFNm5UrV2bUqFE1P1t69OiR22+/vda4Tz/9dA455JC0bNkyHTt2zNe//vW88cYbNef79++fU045Jaeffnrat2+fgQMH1tzTB/9k8YM5u+OOO3LAAQekefPm6dGjRyZPnlxrrP/5n/9J165d07x58xx66KEZPXp0vT+J+9d569mzZ84+++y8+uqrmTdv3mpXOzz55JMplUqZPn16kvf/JcoXvvCFbL755mnRokV23XXX3H333UlWXS3xwRyuz/fH0qVLc8opp2TLLbdM06ZNs80222TUqFFJkqIocsEFF9SsUO3cuXNOPfXUDffFqwPf+ta3UiqV8uijj2bo0KH55Cc/mV133TUjRozIX/7yl9Wuqpk/f37N78Ek+ec//5mjjjoqn/jEJ9KsWbPssMMONb/vt9122yTJHnvskVKplP79+yd5/3vjoosuylZbbZXq6ur07NkzEyZMqBnjg3FvvfXWfOYzn0mzZs2y11575YUXXshjjz2W3r17p2XLljnkkEMyb968Wvf0cfz5Vq7V/Su9MWPGpFu3bjWfV/KcLUnOOuusfPKTn0zz5s3TvXv3nHvuubXCl3J+Jn7czZ8/P3/605/ywx/+MAcccEC22Wab7L333hk5cmTN85DRo0dn9913T4sWLdK1a9d861vfWuu/pPvd736XPffcM02bNk337t1z4YUX1jw/2xR/7nzYnnvuma5du+aOO+6oOXbHHXdk6623zh577FFzbMKECdlvv/3Stm3bbLHFFvn85z+fadOm1Zyvy9+FH/U1T97/Hfzf//3f+fznP5/mzZtn5513zuTJk/PSSy+lf//+adGiRfbZZ59a9ZXbbyXPoz+u/vU5XZs2bVIqlWod21QWzNS1uv6eWdvvxHnz5qV379459NBDs2TJko98fltfr/c2BR885+3SpUv23HPPnHPOOfnd736X//3f/615Xb+2rOCvf/1rDjjggLRq1SqtW7dOr1698vjjjydZ/QreSy65JB06dEirVq3yjW98I2effXat5wkfPCe4/PLLs+WWW2aLLbbIySefvEkuOPio14Ivvvhi9t9//zRt2jS77LJL7rnnnlrXlvOaI0keeuih9O/fP82bN8/mm2+egQMH5p///GeStX+/rek58YdXfS9ZsiSnnnpqOnTokKZNm2a//fbLY489tkqtH5WbNSR//vOfa57Ld+3aNaeeemreeeedmvM33nhjevfunVatWqVTp0458sgj8/rrr6+2r0mTJmX48OFZsGDBav/F0uLFiz/y58mHre110AfW9tztg++93//+99lll11SXV2dGTNmZMmSJTnzzDPTpUuXtGjRIn369Kl5/bTBFNSL6667rujdu3dRFEVx1113Fdttt12xcuXKoiiK4pZbbimqq6uLn//858Vzzz1X/Nd//VfRqlWrokePHjXXDxs2rGjVqlVx8sknF88991xx3XXXFUmKgQMHFt///veLF154obj44ouLzTbbrHj11VeLoiiKl19+uUhSPPHEE0VRFMX9999fJCn69OlTTJo0qXjmmWeKz3zmM8U+++xTM875559fM+6yZcuKNm3aFGeeeWbx0ksvFX//+9+LG264oXjllVeKxYsXF9/5zneKXXfdtZg9e3Yxe/bsYvHixUVRFEWSokOHDsW4ceOKadOmFa+88kqxdOnS4rzzzisee+yx4h//+Efxy1/+smjevHlxyy23FEVRFIsWLSq+8pWvFIMGDarpb8mSJavcw5lnnlnst99+tb623/nOd2odGzBgQPGFL3yheOyxx4oXXnih+M53vlNsscUWxZtvvrle81QURfGHP/yhaNSoUXHeeecVf//734snn3yy+MEPflBz/itf+UrRtWvX4o477iimTZtW3HvvvcXNN99cFEVRvPbaa0Xz5s2Lb33rW8Wzzz5b/Pa3vy3at29fnH/++TXXb7PNNsUVV1xRq6YePXrUapOk2GqrrYqbbrqpePHFF4tTTz21aNmyZfHmm28Wy5cvL37zm98USYrnn3++mD17djF//vyiKIqiX79+xWmnnVZrrHbt2hVjx44tXnzxxWLUqFFFVVVV8dxzzxVFURQLFiwo2rVrV3zta18rnnnmmeLuu+8uPvnJT9aaj6Ioin/+859FkyZNipkzZ67169sQvPnmm0WpVKo1b//q+OOPLzbffPPijTfeKLbaaqvioosuqvl/siiK4vrrry8222yzYsCAAcVjjz1WTJkypdh5552LI488sqaPX/7yl8WWW25Z/OY3vyn+8Y9/FL/5zW+Kdu3aFTfccENRFP/3vdmtW7eaNrNmzdrwN78ehg0bVnzxi19c4/mP+v+yKIriH//4R7HZZpsVZ555ZvHcc88Vv/71r4suXboUSYp//vOfRVG8/7Vt06ZNTZ/nn39+0bJly+JLX/pS8be//a148MEHi06dOhXnnHNOTZtLLrmk2GmnnYoJEyYU06ZNK66//vqiurq6mDRpUlEU7///+YlPfKIYOXJk8eyzzxZTp04tPvvZzxYHHHBATR/9+vUrWrZsWXz3u98tnnvuuZrvgSTFb3/726Io/m/Odtppp+IPf/hD8fzzzxdf/vKXi2222aZYtmxZURRF8ec//7moqqoqLrvssuL5558vxo4dW7Rr167WPW1sH563RYsWFd/85jeL7bffvlixYkXN74UP5qAoiuKJJ54okhQvv/xyURRFMXjw4OKzn/1s8dRTTxXTpk0r7rrrruKBBx4oiqJY5fq6+P647LLLiq5duxYPPvhgMX369OJPf/pTcdNNNxVFURS33XZb0bp16+Luu+8uXnnlleKRRx4prr322g33BVxPa/t5UxSr/q4uivf/v01S3H///UVRFMXJJ59c9OzZs3jssceKl19+ubjnnnuK3//+90VRFMWjjz5aJCnuvffeYvbs2TXfc6NHjy5at25d/PrXvy6ee+654j//8z+LzTbbrHjhhRdqjfvB98/f//734tOf/nTRq1evon///sWf//znYurUqcX2229fnHjiiTW1fRx/vn1gTT/n/vX/8399nvSBK664othmm21q9bOuz9mKoiguvvji4qGHHipefvnl4ve//33RsWPH4oc//GHN+XJ+Jn7cLVu2rGjZsmVx+umnF++9995q21xxxRXFfffdV7z88svFxIkTix133LE46aSTas5/+HfNgw8+WLRu3bq44YYbimnTphX/7//9v6Jbt27FBRdcUBTFpvdz58M++P969OjRxUEHHVRz/KCDDiquuOKK4otf/GIxbNiwoiiK4vbbby9+85vfFC+++GLxxBNPFF/4wheK3XffvVixYkVRFHX3u3BtX/OieP93cJcuXYpbbrmleP7554shQ4YU3bp1Kw488MBaP7MGDRq0zv1W8jz642Z1P+8+/L3x72pDfM98+Hfiv36tZ8yYUey4447FsGHDiuXLlxdF8dHPb+vj9d6m4KNeq/To0aM45JBDiqJYe1aw6667Fl/72teKZ599tnjhhReKW2+9tXjyySeLolj1e+SXv/xl0bRp02LcuHHF888/X1x44YVF69atV8lxWrduXZx44onFs88+W9x1111F8+bNN6nfI0Xx0V/fFStWFLvttltx0EEHFU8++WTxwAMPFHvssUet11LlvOZ44okniurq6uKkk04qnnzyyeLpp58ufvKTnxTz5s0rimLt329rek784dpPPfXUonPnzsXdd99dPPPMM8WwYcOKzTffvKZ9ObnZhlbOc9KiKIqXXnqpaNGiRXHFFVcUL7zwQvHQQw8Ve+yxR3HMMcfUXHPdddcVd999dzFt2rRi8uTJRd++fWu+Hz7c55IlS4oxY8YUrVu3rslAFi1aVBTF2n+efFg5r4M+UM5zt80226zYZ599ioceeqh47rnninfeeaf4xje+Ueyzzz7Fgw8+WLz00kvFZZddVlRXV9e83tkQhOr1ZJ999inGjBlTFMX7Lwjat29f82K5b9++xbe+9a1a7fv06bPKD+Ntttmm5gdGURTFjjvuWHzmM5+p+Xz58uVFixYtil//+tdFUaw5VL/33ntrrhk/fnyRpHj33XeLoqgdqr/55ptFkppw6sNW98KyKN5/snr66aev9Wty8sknF0OHDq11jx/+wfHhe3jiiSeKUqlUvPLKK0VRvP8DvEuXLsXPfvazoiiK4k9/+lPRunXrVV5obbfddsV///d/r7Wmj5qnonh/ro466qjVXvv8888XSYp77rlntefPOeecYscdd6wV0v//7d13XNfW/vjxF3vIUCwiWBVEQRygqChy62AUHFS/oiKlFRW14qijLmpdVeuq2lpHrQNqxXHdXhEUZy16nVeqXsRRR71Wqai1uMH8/uBHykc+wEcEFft+Ph48HiQ5OTlJPklO3jk5mT9/vmJhYaHuV12D6p999pk6nJmZqQBKQkKCoijaL1iKor2S9cEHH6jDT58+VSpVqqRuy4ULFyoVK1ZUfxuKoiiLFy/OV8mKi4tTH0SUBf/+9781LvDPmj17tgIoN27c0Lo/YmJiFEA5f/68Om7+/PmKnZ2dOuzs7KwGAXNNmjRJ8fb2VhTlr9917m+tLIiIiFAMDAyUcuXKafxNmTJFUZSif5ejRo1S6tWrp5HnmDFjigyqm5ubK3fv3lXHjRgxQmnatKmiKIry8OFDxdzcXDlw4IBGvpGRkUpYWJiiKDnb/d1339WY/uuvv6o3IoqSc2w0bNgw3zprC6ovWbJEnX769GkFUFJTUxVFUZTQ0FClXbt2GnmEh4e/8qB63v0GKPb29sqxY8cURdGtglu/fn2NgERe2oLqL3p8DBo0SPH19dU4V+aaNWuW4uLiojx+/Pi5t8WrcOjQIQVQNmzYUGAaXYLqwcHBSs+ePXWeX1EUxcHBQT0+czVp0kStb2j7Ta9atUoBlF27dqnjpk6dqri6uqrDb+L5LVdB5zlTU9PnDqo/b51Nm5kzZyqNGjVSh4s6J/5drFu3TqlQoYJiamqqNG/eXImOjlZSUlIKTL927VqlYsWK6vCz1xo/P798N3w//PCDYm9vryhK2TvvPCu3fp2enq6YmJgoly5dUi5duqSYmpoqv//+u0aA8Fm///67AignT55UFKXkroVFbXNFyV+vOHjwoAIoS5cuVcetWrVKMTU1faF8da1Hv2kkqF6w0jhmnr0m5m7rM2fOKFWrVlU+/vhjtd6jS/32Zd/vlQWFBX1DQ0MVNzc3nWIFlpaWakOBZz17jDRt2lQZMGCARhofHx+tcZzcByaKoihdunRRQkNDn2PtXr3C7gW3b9+uGBoaajSyS0hIeO6gelhYmOLj46NzmQo63p797eb9bWRmZipGRkZKXFycOv3x48eKg4ODMmPGDI2yFhY3K2261EkVJee80LdvX4159+/fr+jr6xdY1iNHjiiAGizXdj+n7VpQ1PnkWbrcBxVEW90NUB9wKYqiXL58WTEwMMjXuNPPz0+Jjo5+7mXqSrp/eQXS0tI4fPgwYWFhABgaGhIaGqp2C5KamkrTpk015vH29s6XT926ddHX/2sX2tnZUb9+fXXYwMCAihUrFvgqRy53d3f1f3t7ewCt89jY2NCjRw8CAwMJDg7m66+/zvcKf0EaN26cb9z8+fNp1KgRtra2WFhY8N1333HlyhWd8svVoEED3NzcWLlyJZDTnUp6ejpdunQBcl7XyszMpGLFilhYWKh/Fy9ezPd66LOK2k+Q84qSn5+f1vlPnDiBgYEBLVu21Do9NTUVb29vjW5EfHx8yMzM5OrVq7pvBDT3Ybly5bCysipyvxeVT+7rnrn5pKWl4e7ujqmpqZrGy8srXx5lreuXXIqiFHtec3NznJ2d1WF7e3t1u927d48LFy4QGRmp8RucPHlyvt+gtuPkdda6dWtOnDih8Zf3I06F/S7T0tJo0qSJRn7afk/PcnR01OgbMu+2Pn/+PPfv3ycgIEBjWy9fvlzd1ikpKezZs0djeu3atQE09kejRo102gaFnT/T0tLyrZMu61ja8u63w4cPExgYSJs2bdQus4ry8ccfM3nyZHx8fBg/fnyRHzl90eOjR48enDhxAldXVz7++GN27Nih5tWlSxcePHhAjRo16NOnDxs3biyRPjJLy4ucZ/KKiopi9erVNGjQgJEjR3LgwIFC09+9e5dr167h4+OjMd7Hx4fU1FSNcXl/03Z2dgAadQs7O7u/xfktl7bz3JIlS547n+LU2dasWYOPj4/a5cJnn32Wr55U2Dnx7yIkJIRr166xZcsWgoKC2Lt3L56enuqr/Tt37sTPz48qVapgaWnJhx9+SEZGBvfv39eaX0pKCp9//rnGb7pPnz789ttv3L9/v8yddwpia2urdm0YExNDu3bteOuttzTSnDt3jrCwMGrUqIGVlZXardGzv8MXvRYWtc21Laeg89PDhw+5e/dusfN9kXq0eLOV5DGj7Zr44MED3nnnHTp16sTXX3+t3iPqUr99HiVxv1fWKYqCnp6eTrGCYcOG0bt3b/z9/Zk2bVqh21zXun/dunUxMDBQh8vqtbuge8HU1FSqVq2Kg4ODmlZbPKsohcVaQPfjrTAXLlzgyZMnGnVkIyMjvLy8Cq0jFxY3Ky261ElTUlKIjY3V+D0HBgby9OlTLl68CMCxY8cIDg6mWrVqWFpaqvGq543FQeHnk2c9z32QLnU3Y2NjjeWfPHmS7OxsXFxcNNZ/3759xTpX6urlfe1RqJYuXUpWVpbGSUZRFExMTJg3b57O+RgZGWkM537Z+tlxRXXYn3ee3It3QfPExMTw8ccfk5iYyJo1a/jss89ISkqiWbNmhS6jXLlyGsOrV69m+PDhzJo1C29vbywtLZk5cyaHDh0qNB9twsPDWblyJaNHj2blypUEBQWpH1fNzMzE3t5eaz9KRfVrXNR+sra2xszMrMD5C5umK319/XwnH239rRVnv2vzovk8fvyYxMREPv300+de9qtSs2ZN9PT0SE1N1egPP1dqaioVKlTA1ta2wDy0bbfc/Zbb99fixYvzPSzLW5mC/MfJ665cuXLUrFmzwOkl9bvUNc/cbR0fH0+VKlU00uV+YDQzM5Pg4GCmT5+eL+/cyhHovi+e5/z5unh2vy1ZsgRra2sWL17Mu+++C2hWep495/Tu3ZvAwEDi4+PZsWMHU6dOZdasWQwaNEjr8l70+PD09OTixYskJCSwc+dOunbtir+/P+vWraNq1aqkpaWxc+dOkpKS6N+/PzNnzmTfvn35lvs6qFWrFnp6epw5c6bANLmB18L2Qe5DkG3btpGUlISfnx8DBgzgyy+/fOEyavtNPzvu2WPuTTy/5dJ2nsv74PtFrtOFnc8OHjxIeHg4EydOJDAwEGtra1avXs2sWbOKzPd1PweVBlNTUwICAggICGDs2LH07t2b8ePH06pVK9q3b09UVBRTpkzBxsaGn376icjISB4/foy5uXm+vDIzM5k4cSKdOnXSupyydt4pTK9evRg4cCCQ09jlWcHBwVSvXp3Fixfj4ODA06dPqVevnvoB71wvei0sapsXtpzCll2cfHPz+TseR6JoJXXMaLsmmpiY4O/vz9atWxkxYoRal9Wlfvs85Peec3/n5OSkU6xgwoQJvP/++8THx5OQkMD48eNZvXq11vtGXb0p+6Coe8HC6FLfLSqeouvxVlJe9X1fUXVSyDlffPTRR1q/9VKtWjXu3btHYGAggYGBxMXFYWtry5UrVwgMDCzWdnue37Iu90GQ850IXepuZmZmGg1UMzMzMTAw4NixY/nuQ0rzeyDSUv0ly8rKYvny5cyaNUvjCVNKSgoODg6sWrUKNze3fMHlvJ32v2oNGzYkOjqaAwcOUK9ePbWVuLGxMdnZ2TrlkZycTPPmzenfvz8NGzakZs2a+Z4e6Zrf+++/z6lTpzh27Bjr1q0jPDxcnebp6cn169cxNDSkZs2aGn/PtizIS5f9BDlP5nbt2qU1j/r16/P06VP27dundXruB5byXkiSk5OxtLTk7bffBnJaROR9G+Du3bvqE0ZdGRsbA+i8bwri6urKyZMnefTokTou7wc8IOdDFhUqVMDDw+OFlvUyVaxYkYCAABYsWMCDBw80pl2/fp24uDhCQ0PR09N7rt94Ljs7OxwcHPjll1/y/QZzP57yd+Tq6qp+5CfXs7+n55X3IyXPbuuqVasCOeeE06dP4+jomC9NSQf9XF1d863Ti65jadDT00NfX58HDx6oD4/ynne0fZiqatWq9OvXjw0bNvDJJ5+wePHiYi1b1+PDysqK0NBQFi9ezJo1a1i/fj23bt0CcipUwcHBzJ07l71793Lw4EFOnjxZrPKUNhsbGwIDA5k/f77GB4Ny3blzR+d9YGtrS0REBCtWrOCrr75SPwyk7ZxvZWWFg4MDycnJGnkkJydTp06dYq+PnN9y9sP169c1ruUl8TG3AwcOUL16dcaMGUPjxo2pVauWzm+TiJzrwb179zh27BhPnz5l1qxZNGvWDBcXF65du1bovJ6enqSlpeX7TdesWVMNApSl805hgoKCePz4MU+ePFE/yp0rIyODtLQ0PvvsM/z8/HBzc1M/Dvc8dLkW6rLNi6Mk8i2perR4M5TmMaOvr88PP/xAo0aNaN26tXqu0qV++zLv98q63bt3c/LkSUJCQnSOFbi4uDB06FB27NhBp06d8n1YPFdZqfuXNjc3N3799VeNuuyz8Sxd6ruFxVp0Od50OS6cnZ0xNjbWqCM/efKEI0eOvFAd+VXx9PTkv//9r9brnrGxMWfOnCEjI4Np06bxzjvvULt27SJb2xcnBqKNLvdBQLHqbpATp8zOziY9PT3fuleuXPmFy18Qaan+km3dupXbt28TGRmJtbW1xrSQkBCWLl3K8OHD6dGjB40bN8bHx4e4uDhOnz5NjRo1XlGpc1y8eJHvvvuO9957DwcHB9LS0jh37hzdu3cHcl5BvnjxIidOnODtt9/G0tKywKfntWrVYvny5Wzfvh0nJyd++OEHjhw5onET7ujoyPbt20lLS6NixYr5tlfedM2bNycyMpLs7GyNrkf8/f3x9vamY8eOzJgxQz0g4+Pj+b//+78CX0fXZT/169eP8ePH4+fnh7OzM926dSMrK4tt27YxatQoHB0diYiIoFevXsydOxcPDw8uX75Meno6Xbt2pX///nz11VcMGjSIgQMHkpaWxvjx4xk2bJha0ff19SU2Npbg4GDKly/PuHHj8j11K0r16tXR09Nj69attG3bFjMzs2I9qXv//fcZM2YMffv2ZfTo0Vy5ckVtFZn7hHDLli1lsuuXefPm0bx5cwIDA5k8eTJOTk6cPn1abSUyZcoUIOe39uOPP9KtWzdMTEwKfTCT18SJE/n444+xtrYmKCiIR48ecfToUW7fvs2wYcNKc9VK1aNHj7h+/brGOENDQ522y0cffcTs2bMZNWoUkZGRnDhxQn1VP+8T5+dhaWnJ8OHDGTp0KE+fPuUf//gHf/zxB8nJyVhZWREREcGAAQNYvHgxYWFhjBw5EhsbG86fP8/q1atZsmTJcx9fhRk0aBAtWrRg9uzZBAcHs3v3bhISEoq9fiUl7367ffs28+bNU1vw596gTZgwgSlTpnD27Nl8LWOHDBlCmzZtcHFx4fbt2+zZswc3N7dil6eo42P27NnY29vTsGFD9PX1Wbt2LZUrV6Z8+fLExsaSnZ1N06ZNMTc3Z8WKFZiZmVG9evUX2kalaf78+fj4+ODl5cXnn3+Ou7s7WVlZJCUlsXDhQlJTU2nWrBnTpk3DycmJ9PR0PvvsM408xo0bR6NGjahbty6PHj1i69at6j6oVKkSZmZmJCYm8vbbb2Nqaoq1tTUjRoxg/PjxODs706BBA2JiYjhx4gRxcXEvtD5v6vlNV61ateL3339nxowZdO7cmcTERBISErCysnqhfGvVqsWVK1dYvXo1TZo0IT4+no0bN5ZQqd8cGRkZdOnShV69euHu7o6lpSVHjx5lxowZdOjQgZo1a/LkyRO++eYbgoODSU5O5ttvvy00z3HjxtG+fXuqVatG586d0dfXJyUlhVOnTjF58uQyed4piIGBgfp6+7PXvwoVKlCxYkW+++477O3tuXLlCqNHj37uZehyLSxqmxdXSeRbUvVo8WYo7WPGwMCAuLg4wsLC8PX1Ze/evVSuXLnI+u3LvN8rS3LrvNnZ2dy4cYPExESmTp1K+/bt6d69O/r6+oXGCurWrcuIESPo3LkzTk5OXL16lSNHjhASEqJ1eYMGDaJPnz40btyY5s2bs2bNGn7++edXHscpLQXdC/r7++Pi4kJERAQzZ87k7t27jBkzRiOdLvcc0dHR1K9fn/79+9OvXz+MjY3Zs2cPXbp0wcbGpsjjraA6cV7lypUjKiqKESNGYGNjQ7Vq1ZgxYwb3798nMjKyBLfWyzFq1CiaNWvGwIED6d27N+XKleO///0vSUlJzJs3j2rVqmFsbMw333xDv379OHXqFJMmTSo0T0dHRzIzM9m1axceHh6Ym5trfdNPF7rcBxWn7gY5D7/Cw8Pp3r07s2bNomHDhvz+++/s2rULd3d32rVrV6wyF0Vaqr9kS5cuxd/fX2uAOCQkhKNHj+Lm5sbYsWMZOXIkjRo14vLly0RFRb2C0moyNzfnzJkzhISE4OLiQt++fRkwYAAfffQRkFP+oKAgWrduja2trdqaW5uPPvqITp06ERoaStOmTcnIyKB///4aafr06YOrqyuNGzfG1tY2Xwu7vMLDw0lJSeH//u//NF4T0tPTY9u2bbRo0YKePXvi4uJCt27duHz5stoXoza67Keff/6ZVq1asXbtWrZs2UKDBg3w9fXl8OHDatqFCxfSuXNn+vfvT+3atenTp4/6VK5KlSps27aNw4cP4+HhQb9+/YiMjNQInkRHR9OyZUvat29Pu3bt6Nixo0bfxLqoUqUKEydOZPTo0djZ2amvLD4vKysr/vWvf3HixAkaNGjAmDFjGDduHPDXK7RlNaheq1Ytjh49So0aNejatSvOzs707duX1q1bc/DgQWxsbAD4/PPPuXTpEs7OzoV2B/Os3r17s2TJEmJiYqhfvz4tW7YkNja2zLfkTExMxN7eXuPvH//4h07zOjk5sW7dOjZs2IC7uzsLFy5UK1vFeZU116RJkxg7dixTp07Fzc2NoKAg4uPj1W2d21o3Ozubd999l/r16zNkyBDKly//Qq3htPHx8eHbb79l9uzZeHh4kJiYyNChQzVeOX8V8u63pk2bcuTIEdauXUurVq0wMjJi1apVnDlzBnd3d6ZPn54v8JCdnc2AAQPU7evi4sKCBQuKXZ6ijg9LS0tmzJhB48aNadKkCZcuXWLbtm3o6+tTvnx5Fi9ejI+PD+7u7uzcuZN//etfahdgr6MaNWpw/PhxWrduzSeffEK9evUICAhg165dLFy4EIBly5aRlZVFo0aNGDJkSL59YGxsTHR0NO7u7rRo0QIDAwNWr14N5NzMzJ07l0WLFuHg4ECHDh2AnL7whw0bxieffEL9+vVJTExky5Yt1KpV64XW5009v+nKzc2NBQsWMH/+fDw8PDh8+DDDhw9/4Xzfe+89hg4dysCBA2nQoAEHDhxg7NixJVDiN4uFhQVNmzZlzpw5tGjRgnr16jF27Fj69OnDvHnz8PDwYPbs2UyfPp169eoRFxfH1KlTC80zMDCQrVu3smPHDpo0aUKzZs2YM2eOGjQvi+edwlhZWWl9CKSvr8/q1as5duwY9erVY+jQocycOfO589flWljUNi+uksi3pOrR4s1R2seMoaEhq1atom7duvj6+pKenl5k/fZl3u+VJbl1XkdHR4KCgtizZw9z585l8+bNGBgYFBkrMDAwICMjg+7du+Pi4kLXrl1p06YNEydO1Lq88PBwoqOjGT58uNp9YY8ePcrkttNFQfeC+vr6bNy4kQcPHuDl5UXv3r3VRmq5dLnncHFxYceOHaSkpODl5YW3tzebN2/G0NBQp+OtoDrxs6ZNm0ZISAgffvghnp6enD9/nu3bt1OhQoWS3WAvgbu7O/v27ePs2bO88847NGzYkHHjxqldGtva2hIbG8vatWupU6cO06ZNK7L7yObNm9OvXz9CQ0OxtbVlxowZxS6fLvdBxam75YqJiaF79+588sknuLq60rFjR44cOUK1atWKXeai6Ckl9dUsIcTfTlxcHD179uSPP/4gNTUVX19ffv/99zLXp6h4PUyZMoVvv/2WX3/99VUXpdT06dOHM2fOsH///lddFCGEEOKVkGuhEGVH3vu9kvhm2N9NQEAAlStX5ocffnjVRRFClALp/kUIobPly5dTo0YNqlSpQkpKCqNGjaJr166YmZmRlZXFN998IwF1obMFCxbQpEkTKlasSHJyMjNnznzjWoB9+eWXBAQEUK5cORISEvj+++9fqFW3EEIIUdbItVCIsqOw+z1RuPv37/Ptt98SGBiIgYEBq1atUj9qLYR4M0lQXQihs+vXrzNu3DiuX7+Ovb09Xbp0UV/l8vLywsvL6xWXUJQl586dY/Lkydy6dYtq1arxySefEB0d/aqLVaIOHz7MjBkz+PPPP6lRowZz586ld+/er7pYQgghxEsj10Ihyo7C7vdE4XK7k5kyZQoPHz7E1dWV9evX4+/v/6qLJoQoJdL9ixBCCCGEEEIIIYQQQgihI/lQqRBCCCGEEEIIIYQQQgihIwmqC1FGTJgwgQYNGqjDPXr0oGPHjoXOs3fvXvT09Lhz506plk0I8ffTqlUrhgwZ8qqLIUrIs9cYIYQQQgghhNBVbGws5cuXLzTNm3bPIUF1IUrJwYMHMTAwoF27dqWS/9dff01sbKw6rC3A1bx5c3777Tesra1LpQxCiJerR48e6Onpoaenh5GREXZ2dgQEBLBs2TKePn36qotXpr0uDwl+//13oqKiqFatGiYmJlSuXJnAwECSk5NLdbnDhw9n165dpbqM18Wzx5GTkxMjR47k4cOHr7poQgghhBBCvLC89V1jY2Nq1qzJ559/TlZWVqktMzQ0lLNnz5Za/q8j+VCpEKVk6dKlDBo0iKVLl3Lt2jUcHBxKNH9dAuXGxsZUrly5RJcrhHi1goKCiImJITs7mxs3bpCYmMjgwYNZt24dW7ZswdBQLu1lWUhICI8fP+b777+nRo0a3Lhxg127dpGRkVGs/B4/foyxsXGR6SwsLLCwsCjWMsqi3OPoyZMnHDt2jIiICPT09Jg+ffqrLppWuu5HIYQQQggh4K/67qNHj9i2bRsDBgzAyMiI6OhojXQlVc80MzPDzMzshfMpypMnTzAyMir15ehCWqoLUQoyMzNZs2YNUVFRtGvXTqNFubZXYjZt2oSenp7GuGnTpmFnZ4elpSWRkZH5WtDl7f6lR48e7Nu3j6+//lp9Gnnp0qV83b/kLnv79u24ublhYWFBUFAQv/32m5pvVlYWH3/8MeXLl6dixYqMGjWKiIiIIruaEUK8HLmtl6tUqYKnpyeffvopmzdvJiEhQT3X3Llzh969e2Nra4uVlRW+vr6kpKQAcPbsWfT09Dhz5oxGvnPmzMHZ2VkdPnXqFG3atMHCwgI7Ozs+/PBDbt68WWC5bt++Tffu3alQoQLm5ua0adOGc+fOqdNzzz+bNm2iVq1amJqaEhgYyK+//qqmyX0dcNmyZVSrVg0LCwv69+9PdnY2M2bMoHLlylSqVIkpU6ZoLLuw9c2b7w8//ICjoyPW1tZ069aNP//8Eyj4HPqy3blzh/379zN9+nRat25N9erV8fLyIjo6mvfee++51nXJkiU4OTlhamrKd999h4ODQ763GTp06ECvXr005str2bJl1K1bFxMTE+zt7Rk4cKBGWQsrx+su9ziqWrUqHTt2xN/fn6SkJACePn3K1KlTcXJywszMDA8PD9atW6fOe/v2bcLDw7G1tcXMzIxatWoRExOjTj958iS+vr6YmZlRsWJF+vbtS2Zmpjpd21sRHTt2pEePHuqwo6MjkyZNonv37lhZWdG3b18AkpOTadWqFebm5lSoUIHAwEBu375dIuUWQgghhBBvjtz6bvXq1YmKisLf358tW7aosaQpU6bg4OCAq6srUHgddseOHZiamubrWnjw4MH4+voC2mNdRcW1AJYsWYKbmxumpqbUrl2bBQsWqNMuXbqEnp4ea9asoWXLlpiamhIXF1eCW+nFSFBdiFLwz3/+k9q1a+Pq6soHH3zAsmXLUBTlueafMGECX3zxBUePHsXe3l7jxPKsr7/+Gm9vb/r06cNvv/3Gb7/9RtWqVbWmvX//Pl9++SU//PADP/74I1euXGH48OHq9OnTpxMXF0dMTAzJycncvXuXTZs26Vx2IcTL5+vri4eHBxs2bACgS5cupKenk5CQwLFjx/D09MTPz49bt27h4uJC48aN81VG4uLieP/994GcgKmvry8NGzbk6NGjJCYmcuPGDbp27VpgGXr06MHRo0fZsmULBw8eRFEU2rZty5MnT9Q09+/fZ8qUKSxfvpzk5GTu3LlDt27dNPK5cOECCQkJJCYmsmrVKpYuXUq7du24evUq+/btY/r06Xz22WccOnRInaew9c2b76ZNm9i6dStbt25l3759TJs2DXi+c2hpym0tvmnTJh49eqQ1jS7rev78edavX8+GDRs4ceIEXbp0ISMjgz179qhpbt26RWJiIuHh4VqXs3DhQgYMGEDfvn05efIkW7ZsoWbNms9VjrLi1KlTHDhwQG2hM3XqVJYvX863337L6dOnGTp0KB988AH79u0DYOzYsfz3v/8lISGB1NRUFi5cyFtvvQXAvXv3CAwMpEKFChw5coS1a9eyc+dOjQcSuvryyy/x8PDgP//5D2PHjuXEiRP4+flRp04dDh48yE8//URwcDDZ2dkvXG4hhBBCCPFmMzMz4/HjxwDs2rWLtLQ0kpKS2Lp1a5F1WD8/P8qXL8/69evV/LKzs1mzZk2B9xO6xLXi4uIYN24cU6ZMITU1lS+++IKxY8fy/fffa6QbPXo0gwcPJjU1lcDAwJLcLC9GEUKUuObNmytfffWVoiiK8uTJE+Wtt95S9uzZoyiKosTExCjW1tYa6Tdu3KjkPRy9vb2V/v37a6Rp2rSp4uHhoQ5HREQoHTp0UIdbtmypDB48WGOePXv2KIBy+/ZtddmAcv78eTXN/PnzFTs7O3XYzs5OmTlzpjqclZWlVKtWTWNZQohX49njPq/Q0FDFzc1N2b9/v2JlZaU8fPhQY7qzs7OyaNEiRVEUZc6cOYqzs7M6LS0tTQGU1NRURVEUZdKkScq7776rMf+vv/6qAEpaWpqiKJrnnLNnzyqAkpycrKa/efOmYmZmpvzzn/9UFOWv88+///1vNU1qaqoCKIcOHVIURVHGjx+vmJubK3fv3lXTBAYGKo6Ojkp2drY6ztXVVZk6daqiKIpO66st3xEjRihNmzZVh7WdQ1+FdevWKRUqVFBMTU2V5s2bK9HR0UpKSoqiKLqvq5GRkZKenq6RpkOHDkqvXr3U4UWLFikODg7qdh0/frzGNcbBwUEZM2aM1jLqUo7XWUREhGJgYKCUK1dOMTExUQBFX19fWbdunfLw4UPF3NxcOXDggMY8kZGRSlhYmKIoihIcHKz07NlTa97fffedUqFCBSUzM1MdFx8fr+jr6yvXr19XFEX7b61Dhw5KRESEOly9enWlY8eOGmnCwsIUHx8frct90XILIYQQQog3R977xqdPnypJSUmKiYmJMnz4cCUiIkKxs7NTHj16pKbXpQ47ePBgxdfXV52+fft2xcTERCPelDfWpUtcy9nZWVm5cqVGmkmTJine3t6KoijKxYsXFUCNr71upKW6ECUsLS2Nw4cPExYWBoChoSGhoaEsXbpU5zxSU1Np2rSpxjhvb+8SKZ+5ublGFw/29vakp6cD8Mcff3Djxg28vLzU6QYGBjRq1KhEli2EKD2KoqCnp0dKSgqZmZlUrFhRbflsYWHBxYsXuXDhAgDdunXj0qVL/Pvf/wZyWgh4enpSu3ZtAFJSUtizZ4/G/LnTcvPIKzU1FUNDQ43zVsWKFXF1dSU1NVUdZ2hoSJMmTdTh2rVrU758eY00jo6OWFpaqsN2dnbUqVMHfX19jXG55y1d1ldbvnnPfa+TkJAQrl27xpYtWwgKCmLv3r14enoSGxur87pWr14dW1tbjXzDw8NZv3692gI+Li6Obt26aWzXXOnp6Vy7dg0/Pz+tZdS1HK+z1q1bc+LECQ4dOkRERAQ9e/YkJCSE8+fPc//+fQICAjTWbfny5eq6RUVFsXr1aho0aMDIkSM5cOCAmm9qaioeHh6UK1dOHefj48PTp09JS0t7rjI2btxYYzi3pbo2L1puIYQQQgjxZtm6dSsWFhaYmprSpk0bQkNDmTBhAgD169fX6EddlzpseHg4e/fu5dq1a0DO/US7du3ydfmSN8/C4lr37t3jwoULREZGatRfJ0+enO+e4tl68etCvmYmRAlbunQpWVlZGh8mVRQFExMT5s2bh76+fr6uYPJ2j1Danv2gg56e3nN1TSOEeD2lpqbi5OREZmYm9vb27N27N1+a3ApP5cqV8fX1ZeXKlTRr1oyVK1cSFRWlpsvMzCQ4OFjrRxvt7e1LaxUA7ecobeNy+wfXZX0LyvfZPsZfF6ampgQEBBAQEMDYsWPp3bs348ePp3///jqta97KcK7g4GAURSE+Pp4mTZqwf/9+5syZo3X5RX1gSNdt/jorV66c2p3NsmXL8PDwYOnSpdSrVw+A+Ph4qlSpojGPiYkJAG3atOHy5cts27aNpKQk/Pz8GDBgAF9++aVOy9a1HvDsfixsv+T2d1ma5RZCCCGEEGVH69atWbhwIcbGxjg4OGBo+FcIWNv9QlGaNGmCs7Mzq1evJioqio0bN2p8P/B55dZfFy9enC/4bmBgoDFcnPK+DBJUF6IEZWVlsXz5cmbNmsW7776rMa1jx46sWrWK6tWr8+eff3Lv3j31xHDixAmNtG5ubhw6dIju3bur43JblBbE2NhY7Ve1uKytrbGzs+PIkSO0aNECyOkn6/jx4/k+YCeEeH3s3r2bkydPMnToUN5++22uX7+OoaEhjo6OBc4THh7OyJEjCQsL45dfftHo29zT05P169fj6OioUfkqiJubG1lZWRw6dIjmzZsDkJGRQVpaGnXq1FHTZWVlcfToUfVtmLS0NO7cuYObm1sx1zynrLqsb1FK4hxaWurUqcOmTZteaF1NTU3p1KkTcXFxnD9/HldXVzw9PbWmtbS0xNHRkV27dtG6det800tqm78u9PX1+fTTTxk2bBhnz57FxMSEK1eu0LJlywLnsbW1JSIigoiICN555x1GjBjBl19+iZubG7GxsRrX+OTkZPT19dWPQNna2mp8IDw7O5tTp05p3dZ5ubu7s2vXLiZOnJhvWp06dV6o3EIIIYQQ4s2StxFJUXSpw0LOPWRcXBxvv/02+vr6tGvXrtA8C4tr2dnZ4eDgwC+//FJgv+yvO+n+RYgStHXrVm7fvk1kZCT16tXT+AsJCWHp0qU0bdoUc3NzPv30Uy5cuMDKlSvzPd0bPHgwy5YtIyYmhrNnzzJ+/HhOnz5d6LIdHR05dOgQly5d4ubNm8VugTlo0CCmTp3K5s2bSUtLY/Dgwdy+fRs9Pb1i5SeEKFmPHj3i+vXr/O9//+P48eN88cUXdOjQgfbt29O9e3f8/f3x9vamY8eO7Nixg0uXLnHgwAHGjBnD0aNH1Xw6derEn3/+SVRUFK1bt9Z4u2bAgAHcunWLsLAwjhw5woULF9i+fTs9e/bUGniuVasWHTp0oE+fPvz000+kpKTwwQcfUKVKFTp06KCmMzIyYtCgQRw6dIhjx47Ro0cPmjVrptHl1PPSdX2LUlLn0BeRkZGBr68vK1as4Oeff+bixYusXbuWGTNm0KFDhxde1/DwcOLj41m2bFmRFdcJEyYwa9Ys5s6dy7lz5zh+/DjffPMNUHLb/HXSpUsXDAwMWLRoEcOHD2fo0KF8//33XLhwQV333A8mjRs3js2bN3P+/HlOnz7N1q1b1QdD4eHhmJqaEhERwalTp9izZw+DBg3iww8/xM7ODsj5sHB8fDzx8fGcOXOGqKgo7ty5U2QZo6OjOXLkCP379+fnn3/mzJkzLFy4kJs3b2JpaflC5RZCCCGEEH9futRhc9MdP36cKVOm0LlzZ/WNSG10iWtNnDiRqVOnMnfuXM6ePcvJkyeJiYlh9uzZpbauJUmC6kKUoKVLl+Lv74+1tXW+aSEhIRw9epSrV6+yYsUKtm3bRv369Vm1apXar1Wu0NBQxo4dy8iRI2nUqBGXL1/W6JpBm+HDh2NgYECdOnWwtbXlypUrxVqHUaNGERYWRvfu3fH29sbCwoLAwEBMTU2LlZ8QomQlJiZib2+Po6MjQUFB7Nmzh7lz57J582YMDAzQ09Nj27ZttGjRgp49e+Li4kK3bt24fPmyRoXI0tKS4OBgUlJS8gVYHRwcSE5OJjs7m3fffZf69eszZMgQypcvr7UPboCYmBgaNWpE+/bt8fb2RlEUtm3bptHtirm5OaNGjeL999/Hx8cHCwsL1qxZ80LbQ9f1LUpJnUNfhIWFBU2bNmXOnDm0aNGCevXqMXbsWPr06cO8efNeeF19fX2xsbEhLS2N999/v9C0ERERfPXVVyxYsIC6devSvn17zp07B5TcNn+dGBoaMnDgQGbMmEF0dDRjx45l6tSpuLm5ERQURHx8PE5OTkDOWw3R0dG4u7vTokULDAwMWL16NZDzG9++fTu3bt2iSZMmdO7cGT8/P+bNm6cuq1evXkRERNC9e3datmxJjRo1imylDuDi4sKOHTtISUnBy8sLb29vNm/erL5NMmnSpGKXWwghhBBC/H3pUocFqFmzJl5eXvz8889FNtLRJa7Vu3dvlixZQkxMDPXr16dly5bExsaq9dfXnZ4inSkLIQrx9OlT3Nzc6Nq1K5MmTXrVxRFClFGxsbEMGTJEpxa5QgghhBBCCCHE60z6VBdCaLh8+TI7duygZcuWPHr0iHnz5nHx4sUiWzUKIYQQQgghhBBCCPF3IN2/CCE06OvrExsbS5MmTfDx8eHkyZPs3LlT+l0VQgghhBBCCCGEEALp/kUIIYQQQgghhBBCCCGE0Jm0VBdCCCGEEEIIIYQQQgghdCRBdSGEEEIIIYQQQgghhBBCRxJUF0IIIYQQQgghhBBCCCF0JEF1IYQQQgghhBBCCCGEEEJHElQXQgghhBDiJdDT00NPT4+9e/e+6qL8LfTo0QM9PT169OjxqosihBBCCCHeMBJUF0IIIYQQIo8JEyaoAfCyxNHRUS13YX9lPci8d+9eJkyYQGxs7KsuihBCCCGE+JsyfNUFEEIIIYQQQpQcU1NTrK2tC5xe2LSyYO/evUycOJGWLVsW+oDA3t4eV1dX7O3tX17hhBBCCCHE34IE1YUQQgghhHiDhIaGSituYOrUqUydOvVVF0MIIYQQQryBpPsXIYQQQgghhBBCCCGEEEJHElQXQgghhBDiOfzxxx98/vnneHp6YmVlhZmZGbVq1SIqKopffvlFpzyuX7/OwIEDcXJywtTUlMqVKxMeHs6ZM2dKufR/uXTpktrP+qVLlwpMl9tX+7Ot35+d/8aNGwwePFhdJzs7O7p161bkOj19+pR//vOfdOzYkSpVqmBiYoKtrS2NGjVi1KhRnDp1SmN5EydOBGDfvn35+ovPW0ZdPlS6d+9eunTpoi73rbfews/Pj5iYGLKzs7XOk9vnfqtWrQDYtWsX7dq1w9bWFlNTU9zc3Jg4cSIPHz4sdL2FEEIIIUTZJd2/CCGEEEIIoaPTp08TFBTE1atXgZz+y42MjDh//jznz58nJiaGuLg4QkJCCszj4sWLhIWFcf36dczMzDAyMuLGjRusXLmSDRs2sHHjRoKCgl7WKpWI06dP06tXL9LT0zE3NwcgPT2dNWvWkJCQwI8//oiHh0e++W7evElISAg//vijOq58+fI8fPiQ48ePc/z4cdLS0ti0aRMGBgbY2dmRmZnJvXv3MDIywsbGRiM/MzMzncs8bNgw5syZA4Cenh7W1tbcuXOH3bt3s3v3blasWMGmTZuwtLQsMI+ZM2cyatQoIKev+sePH3PmzBkmTJjAvn37SEpKwsDAQOcyCSGEEEKIskFaqgshhBBCCKGDP//8k+DgYK5evUqVKlWIj4/n3r173L17lxMnTtCsWTMePXpEeHg4KSkpBeYzdOhQjI2N2bFjB/fu3ePPP//k0KFD1K9fn4cPHxIaGqoG7cuKDz/8kFq1anHkyBHu3btHZmYmSUlJ2Nvbc/fuXQYNGpRvnqysLDp27MiPP/6IiYkJ06dPJz09ndu3b/Pnn3/yv//9j0WLFlGnTh0AqlatyvXr1xk+fDgAzZs35/r16xp/oaGhOpV33rx5akC9b9++XLt2jdu3b/PHH38wZ84cDA0N2b17N3369Ckwj5SUFEaPHs3o0aPVct+5c4dx48YBsGfPHr7//vvn2o5CCCGEEKJskKC6EEIIIYQQOliwYAEXL17EyMiIxMRE2rZti75+TnXaw8ODHTt24OjoyKNHjxgzZkyB+Tx48IDExEQCAgLQ09MDwMvLi507d2JjY8Pdu3df6AOba9asoXLlylr//P39i51vYezs7EhKSqJx48YAGBoa4u/vz6JFiwDYv39/vgcF33//PcnJyejp6bFhwwZGjhyJra2tOt3BwYG+ffvyxRdflGhZHzx4wPjx4wEICwtj0aJFVK5cGYBy5coxZMgQZs+eDeRsy2PHjmnN586dO4wdO5YvvviCt956CwArKysmTpxIp06dAFi1alWJll0IIYQQQrweJKguhBBCCCGEDtasWQNA586dqVevXr7plpaWjBw5EoCEhAT++OMPrfl06dIFNze3fOMrVapEv379NJZVHA8fPuTGjRta/27evFnsfAvzySefaO16pU2bNhgbGwNw8uRJjWnLli0DoG3btrRt27ZUyqVNUlISt27dAnL6R9emf//+2NvbA7By5UqtaUxMTNRW88/q0KEDAD///PMLllYIIYQQQryOJKguhBBCCCFEER4/fqwGSAtr7R0QEADkfHzz+PHjWtP4+voWOH/utIyMDC5evFisskZERKAoita/EydOFCvPojRt2lTreENDQ7X1eW4gG3K6fjly5AgAwcHBpVKmghw9ehTI6U7GxcVFaxoDAwN1X+Smf1bdunWxsLDQOs3BwQHQXGchhBBCCPHmkKC6EEIIIYQQRbh16xbZ2dkAVKlSpcB0b7/9tvp/enq61jSFzZ93WkHzv44K+5inoaEhAE+ePFHHZWRkqMPVq1cv3cI9I3e7FrYf4K99WdB+0GWds7KyilNEIYQQQgjxmpOguhBCCCGEEOKlyu1LXgghhBBCiLJIgupCCCGEEEIUwcbGBgMDA4B8H9zMK++0SpUqaU3zv//9r8D5804raP6SktuaGnL6YS9IQX3DvwgbGxuMjIwAuHz5connX5jc7VrYfsw7vbT3gxBCCCGEKHskqC6EEEIIIUQRjI2NcXd3B2DXrl0Fptu5cycA+vr6eHp6ak2zZ8+eAufPnWZjY4OTk1Nxi6uTChUqqP//+uuvWtOcPXuWO3fulPiyDQ0N8fLyAuBf//rXc82rr59zC6MoSrGW3bhxYyAnaH727FmtabKzs9V90aRJk2ItRwghhBBCvLkkqC6EEEIIIYQOunXrBsC6des4depUvumZmZnMmDEDgLZt22Jtba01n7Vr15KWlpZv/M2bN1m0aBEAoaGhJVXsApUrVw5nZ2cA1q9frzXNlClTSm35kZGRAGzbto1t27bpPJ+VlRVAsYP9AQEBVKxYEYAJEyZoTbNo0SKuXbsGQFhYWLGWI4QQQggh3lwSVBdCCCGEEEIHUVFRODk58eTJE9q0aUNCQgJPnz4F4OTJkwQGBnLx4kVMTEyYPHlygfmYmpoSFBTEzp071dbWR44cwd/fn5s3b2Jpacno0aNfyjrlBoyXLVvGggULePDgAZDTcr13796sWbMGc3PzUln2hx9+yD/+8Q8URSEkJISZM2dy8+ZNdfq1a9eYM2cOo0aN0pivXr16AJw+fZoDBw4893LNzMzUYPqqVavo168fN27cAOD+/fvMnTuXIUOGADkPNxo1alSMtRNCCCGEEG8yCaoLIYQQQgihA0tLS7Zs2UKVKlW4evUqbdu2pVy5clhbW+Pu7s6BAwcwMTFhxYoVeHh4FJjPnDlzePjwIQEBAVhYWGBpaYmXlxcpKSmYmJiwatUqqlWr9lLWadSoUdSpU4cnT54wYMAALCwsqFChAtWqVWP58uXExsZia2tbKss2NDRk48aNvPPOOzx8+JCRI0dSqVIlKlSogKWlJVWqVGHYsGH5WvW3atUKV1dXsrOz8fHxwcbGBkdHRxwdHVm3bp1Oyx44cCBDhw4Fclql29vbY2Njg7W1NYMHD+bJkye0bt2axYsXl/h6CyGEEEKIsk+C6kIIIYQQQuTx+PFjIKdF87Pq1avH6dOnmTBhAg0aNMDQ0JBHjx7h7OxMv379OH36NJ07dy40fycnJ/7zn/8wYMAAbG1tefz4MZUqVSIsLIz//Oc/tGvXrlTWSxsLCwt++uknhg0bhpOTE4aGhhgZGRESEsLBgwfVLm9Ky1tvvcXevXtZsWIFbdq0wdbWlnv37mFubk6jRo0YPXo0X3zxhcY8hoaG7Nq1i969e+Pk5MS9e/e4fPkyly9fJjMzU+dlz549m927dxMSEoKdnR2ZmZlYWlrSunVrli1bRlJSEpaWliW9ykIIIYQQ4g2gpxT3Cz9CCCGEEEK8gSIjI1m2bBnVq1fn0qVLr7o4QgghhBBCiNeMtFQXQgghhBDi/1MUhf379wPQoEGDV1sYIYQQQgghxGtJgupCCCGEEEIAt27dYsCAAZw7dw746yOeQgghhBBCCJGXdP8ihBBCCCH+1g4cOMB7771HRkaGOi44OJjNmzejp6f3CksmhBBCCCGEeB0ZvuoCCCGEEEII8So9fvyYjIwMrKysqF27NuHh4fTv318C6kIIIYQQQgitpKW6EEIIIYQQQgghhBBCCKEj6VNdCCGEEEIIIYQQQgghhNCRBNWFEEIIIYQQQgghhBBCCB1JUF0IIYQQQgghhBBCCCGE0JEE1YUQQgghhBBCCCGEEEIIHUlQXQghhBBCCCGEEEIIIYTQkQTVhRBCCCGEEEIIIYQQQggdSVBdCCGEEEIIIYQQQgghhNCRBNWFEEIIIYQQQgghhBBCCB1JUF0IIYQQQgghhBBCCCGE0NH/A/zyogNWxv5/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking work to double check that we are looking at the mean for each job function (i.e., sum shouldn't equal 1)\n",
        "func_df[('fraudulent', 'mean')].sum()"
      ],
      "metadata": {
        "id": "PQWqZ9i4OrSJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96a763a4-48a6-4022-f390-0fa63a6868a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7554666172872054"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Grouping by Industry to see if there is a relationship between particular job industries and whether or not a job posting is fraudulent\n",
        "*   Done similarly to Job Functions\n",
        "\n"
      ],
      "metadata": {
        "id": "n5C3K88WO1I7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['industry'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8BE5OlOLL58",
        "outputId": "c39aa4ae-ac05-43a2-b9b6-5ac169dbbce9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "131"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['industry'].value_counts()"
      ],
      "metadata": {
        "id": "RFHQXw1aO7hW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "686f9e1b-fb2e-40cb-da86-e6df80cf4ecd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Information Technology and Services    1734\n",
              "Computer Software                      1376\n",
              "Internet                               1062\n",
              "Marketing and Advertising               828\n",
              "Education Management                    822\n",
              "                                       ... \n",
              "Shipbuilding                              1\n",
              "Sporting Goods                            1\n",
              "Museums and Institutions                  1\n",
              "Wine and Spirits                          1\n",
              "Ranching                                  1\n",
              "Name: industry, Length: 131, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# subset industries that only appear over 100 times\n",
        "industry_df = df[df.groupby('industry')['industry'].transform('size')>200]"
      ],
      "metadata": {
        "id": "lmDTMI7wO-ZQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "industry_df['industry'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEhA_zMPBDXb",
        "outputId": "e9d06978-809a-452c-da55-614845a1707e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Information Technology and Services    1734\n",
              "Computer Software                      1376\n",
              "Internet                               1062\n",
              "Marketing and Advertising               828\n",
              "Education Management                    822\n",
              "Financial Services                      779\n",
              "Hospital & Health Care                  497\n",
              "Consumer Services                       358\n",
              "Telecommunications                      342\n",
              "Oil & Energy                            287\n",
              "Retail                                  223\n",
              "Name: industry, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ind_df = industry_df.groupby([industry_df['industry']]).agg({'fraudulent':['mean']}).reset_index()\n",
        "ind_df = ind_df.sort_values(by=[('fraudulent', 'mean')], ascending=False)"
      ],
      "metadata": {
        "id": "-aoezEASPFw1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14,6))\n",
        "plt.bar(ind_df['industry'], ind_df[('fraudulent', 'mean')])\n",
        "plt.margins(x=0.01)\n",
        "plt.xticks([0,1,2,3,4,5,6,7,8,9,10],\n",
        "           ['Oil &\\nEnergy','Hospital\\n& Health\\nCare',\n",
        "            'Telecomm', 'Consumer\\nServices','Marketing &\\nAdvertising',\n",
        "            'Financial\\nServices','Retail',\n",
        "            'IT &\\nServices','Computer\\nSoftware', 'Education\\nManagement',\n",
        "            'Internet'])\n",
        "plt.ylabel('Mean Proportion Fraudulent', fontsize=16)\n",
        "plt.xlabel('Industry', fontsize=16)"
      ],
      "metadata": {
        "id": "sRW625vfPQgB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "outputId": "b7887e14-fab3-4b7b-8aa0-691a0d909df8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Industry')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ind_df[('fraudulent', 'mean')].sum()"
      ],
      "metadata": {
        "id": "zA0e_loDPSMr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "757880e4-8baf-4fac-b327-664eee665ed8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7692560418326805"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Grouping by Required Education Level to see if there is a relationship between particular required education levels and whether or not a job posting is fraudulent\n",
        "*   Done similarly to Job Functions and Industry\n",
        "\n"
      ],
      "metadata": {
        "id": "DWIVNahwPVyA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['required_education'].nunique()"
      ],
      "metadata": {
        "id": "8XxjpZZGLSne",
        "outputId": "36ad58ba-ff4f-44d5-e14c-1a925841ace6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['required_education'].value_counts()"
      ],
      "metadata": {
        "id": "d0lxE4yRPdMu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3378d03d-9b99-418a-f77c-e1766551651d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bachelor's Degree                    5145\n",
              "High School or equivalent            2080\n",
              "Unspecified                          1397\n",
              "Master's Degree                       416\n",
              "Associate Degree                      274\n",
              "Certification                         170\n",
              "Some College Coursework Completed     102\n",
              "Professional                           74\n",
              "Vocational                             49\n",
              "Some High School Coursework            27\n",
              "Doctorate                              26\n",
              "Vocational - HS Diploma                 9\n",
              "Vocational - Degree                     6\n",
              "Name: required_education, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "edu_df = df.groupby([df['required_education']]).agg({'fraudulent':['mean']}).reset_index()\n",
        "edu_df = edu_df.sort_values(by=[('fraudulent', 'mean')], ascending=False)"
      ],
      "metadata": {
        "id": "iA7AY0TnPfQy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plt.margins(x=0.01)\n",
        "plt.bar(edu_df['required_education'], edu_df[('fraudulent', 'mean')])\n",
        "plt.xticks([0,1,2,3,4,5,6,7,8,9,10,11,12], ['Some \\n High School', 'Certification','High \\nSchool or \\nequivalent',\n",
        "                                            'Master\\'s \\nDegree', 'Professional', 'Unspecified', 'Doctorate',\n",
        "                                            'Some \\nCollege', 'Associate \\nDegree', 'Bachelor\\'s \\nDegree',\n",
        "                                            'Vocational', 'Vocational \\n Degree', 'Vocational \\n HS Diploma'])\n",
        "plt.ylabel('Mean Proportion Fraudulent', fontsize=16)\n",
        "plt.xlabel('Education Level', fontsize=16)"
      ],
      "metadata": {
        "id": "z5x5q-5aPho9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "outputId": "c103874d-5945-4e5e-c3fa-c27fdde851b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Education Level')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "edu_df[('fraudulent', 'mean')].sum()"
      ],
      "metadata": {
        "id": "RPpaSjLvPjRK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e2ed6dc-70b5-49f8-d47b-27bdb31ccb4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.215681956451407"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    }
  ]
}