Predict Customer Churn Case Study.ipynb

predict-customer-churn-case-study.ipynb

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        "<a href=\"https://colab.research.google.com/gist/michellebonat/f5ca8f8db3c5715087613fea66ae7dec/predict-customer-churn-case-study.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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        "id": "jrPuC4gOy0o-"
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      "source": [
        "# Case Study: Predict Customer Churn Using Machine Learning\n",
        "Prepared by Michelle Bonat"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fi-v3NSWy0o_"
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      "source": [
        "## 1: Frame the Problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRTQfLFty0pB"
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      "source": [
        "## **Case Study Overview**\n",
        "\n",
        "Many companies would benefit immensely by investigating their customer churn and taking steps to improve it. Why? Simply stated, it usually takes much less resources to keep a customer than to get a new one. So if you have the information you need on why customers are leaving (churning) you can use this proactively to reduce your churn. Let's look at how we can develop this intelligence using machine learning.\n",
        "\n",
        "----\n",
        "##About The Data and The Approach\n",
        "\n",
        "We're told by our colleagues at the hypothetical company that customer churn is at 50% within 3 months. That means that within 3 months of a set of customers that sign up for the paid product, by the end of 3 months half of them will have cancelled. This is an urgent problem we need to help fix with machine learning! \n",
        "\n",
        "## - Dataset:\n",
        "For this dataset I used an IBM Sample Dataset for customer churn from the TelCo industry which can be found [here](https://community.watsonanalytics.com/wp-content/uploads/2015/03/WA_Fn-UseC_-Telco-Customer-Churn.csv)\n"
      ]
    },
    {
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      "source": [
        "## - Project Steps:\n",
        "\n",
        "1. **Talk with stakeholders (S/H) to: **\n",
        "  * **Discuss/determine goals and metrics for the project.**  Is reducing churn the right problem to solve? How specifically shall we measure performance improvement? What is our success goal? (Assume here: it is the right problem, we measure performance overall by reducing customer churn, success is reducing customer churn by 10% in next 6 months).\n",
        "  * **Understand what deliverables are useful for internal stakeholders** (Assume it is churn prediction factors, later a spreadsheet of customer churn predictions, production pipeline and perhaps an internal dashboard).\n",
        "  * **Discuss an iterative approach with timing and deliverables**, at a high level. First see if the data is predictive. Set a goal for what predictive means (Assume here: 80% +). Come back with findings in 2 weeks.\n",
        "2. **Work with SME's (Subject Matter Experts) to come up with a hypothesis.** Here we'll assume that tenure, addt'l products/features, tech support interactions are key; also possibly how they pay.\n",
        "3. **Get the data.** Work with data engineering to get the data, if needed.\n",
        "3. **Talk to my team and resource the project.** Include team personal skills development where possible. In this case for example purposes, assume all the team is booked and I'll do the initial analysis which appears here.\n",
        "3. **Prototype some approaches.** Is the data usable? Can churn be reliably predicted? Do we need other data? Can it eventually be done at scale? \n",
        "4. **When a valid prototype is achieved, begin A/B testing.** If the prototype indicates a workable approach is viable, coordinate with S/H and team on next steps. Put A/B testing in place. \n",
        "5. **Iterate data and models learned from A/B testing and internal model testing.** Get to target success metrics on prediction rate and any other target metrics in order to move forward. \n",
        "6. **Create the production pipeline. **Once an initial model prototype has been established that surpasses the desired performance thresholds, it is ready for the production line. \n",
        "  * Start simple with incremental wins. Expland as it makes sense with priorities and resources. \n",
        "    * Load the flagged users (potential churners) back to the database as a first step \n",
        "    * Eventually, move to automated data injection from a data warehouse \n",
        "    * Refine the model and the data\n",
        "    * Report performance to stakeholders and team on an ongiong basis. Get feedback and discuss next steps. Implement, learn, improve, repeat.\n",
        "7. **Suggest some ways to improve churn in products and processes based on learnings from the activities listed above.**\n",
        "    * These notes are listed at the bottom of this notebook.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
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      "cell_type": "markdown",
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      "source": [
        "## 2: Data we would we like to have:\n",
        "\n",
        "Generally speaking we would like to have as much data as possible with a long history over as many categories as possible. \n",
        "\n",
        "Here are some types of data that are useful in customer churn analysis:\n",
        "* Customer ID or other identification information\n",
        "* Date the customer was acquired\n",
        "* How the customer was acquired (source of sale i.e. referral, web signup, etc.)\n",
        "* Plan type (what subscription they are on)\n",
        "* Cohort analysis by user type (seasonal onboards by marketing campaign or time of year, etc.)\n",
        "* If they use add ons (sush as Online Security or Device Protection)? \n",
        "* Have they set up to pay for their subscription online? \n",
        "* Customer size\n",
        "* Customer segment type (i.e. company user, accountant)\n",
        "* Customer country of residence\n",
        "* Customer state of residence\n",
        "* NPS score (satisfaction level 0-10 from questionnaire) \n",
        "* Time to first success moment (days)\n",
        "* Total number of times logged in\n",
        "* Time since last login\n",
        "* Days since key inflection points (work with SME in marketing/product) this could be days since logging in, since getting their first result from the technology, etc. \n",
        "* Time spent logged in past month\n",
        "* Time spent logged in average/mo for length of subscription\n",
        "* Number of times they have contacted customer service over life of subscription\n",
        "* Number of customer service calls in the prior month that they cancelled\n",
        "* Number of times they opened and clicked on the Help text in the app or online\n",
        "* What they typed into the search box in the help text in the app\n",
        "* Tenure (Lifetime account duration in days)\n",
        "* Total subscription amount paid\n",
        "* Date unsubscribed (timestamp)\n",
        "* How many other products they have from our company\n",
        "* What other products they have from our company, as a separate column for each (yes / no)\n",
        "* Anything else specific to the business of note\n",
        "* Cancelled yes or no (Churn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2lYY3102y0pC"
      },
      "source": [
        "## 3: SetUp: Importing the Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H61EBZgvy0pD"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import missingno as ms\n",
        "from sklearn import model_selection, metrics  #to include metrics for evaluation # this used to be cross_validation\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pylab as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_wZKe4DMfod"
      },
      "source": [
        "# Quiet warnings since this is a demo (it quiets future and deprecation warnings).\n",
        "def warn(*args, **kwargs):\n",
        "    pass\n",
        "import warnings\n",
        "warnings.warn = warn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sknHgWZQy0pI"
      },
      "source": [
        "## 4: Access Data and Clean It Up"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OwHp561YCO-2"
      },
      "source": [
        "We're using the IBM Telco sample dataset for this case study. It provides some of the datapoints from our wish list but not all. \n",
        "\n",
        "I staged the IBM sample dataset on my own S3 bucket on AWS and I'm accessing it from there:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzVASp4_jCrd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "outputId": "c6dad8e0-1ad4-4752-ed9b-789ea0c44af2"
      },
      "source": [
        "!wget https://s3.amazonaws.com/ibm-sample-cust-churn-data/WA_Fn-UseC_-Telco-Customer-Churn.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-02-12 19:46:34--  https://s3.amazonaws.com/ibm-sample-cust-churn-data/WA_Fn-UseC_-Telco-Customer-Churn.csv\n",
            "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.237.205\n",
            "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.237.205|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 977501 (955K) [text/csv]\n",
            "Saving to: ‘WA_Fn-UseC_-Telco-Customer-Churn.csv’\n",
            "\n",
            "WA_Fn-UseC_-Telco-C 100%[===================>] 954.59K   791KB/s    in 1.2s    \n",
            "\n",
            "2019-02-12 19:46:36 (791 KB/s) - ‘WA_Fn-UseC_-Telco-Customer-Churn.csv’ saved [977501/977501]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmOFyHcF00bR",
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          "height": 69
        },
        "outputId": "e3ffd497-e743-429d-ae56-0e98e6b0a0cb"
      },
      "source": [
        "ls -l"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 964\n",
            "drwxr-xr-x 1 root root   4096 Feb 11 17:40 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n",
            "-rw-r--r-- 1 root root 977501 Jan 12 00:54 WA_Fn-UseC_-Telco-Customer-Churn.csv\n"
          ],
          "name": "stdout"
        }
      ]
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    {
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          "height": 334
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        "outputId": "da4a4655-2669-4388-d041-b2bc15d145c9"
      },
      "source": [
        "# Read the data and view the top portion to see what we are dealing with.\n",
        "data=pd.read_csv('WA_Fn-UseC_-Telco-Customer-Churn.csv')\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
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              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>...</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7590-VHVEG</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
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              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5575-GNVDE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3668-QPYBK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7795-CFOCW</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9237-HQITU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
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              "<p>5 rows × 21 columns</p>\n",
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              "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
              "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
              "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
              "3  7795-CFOCW    Male              0      No         No      45           No   \n",
              "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity  ...  DeviceProtection  \\\n",
              "0  No phone service             DSL             No  ...                No   \n",
              "1                No             DSL            Yes  ...               Yes   \n",
              "2                No             DSL            Yes  ...                No   \n",
              "3  No phone service             DSL            Yes  ...               Yes   \n",
              "4                No     Fiber optic             No  ...                No   \n",
              "\n",
              "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
              "0          No          No              No  Month-to-month              Yes   \n",
              "1          No          No              No        One year               No   \n",
              "2          No          No              No  Month-to-month              Yes   \n",
              "3         Yes          No              No        One year               No   \n",
              "4          No          No              No  Month-to-month              Yes   \n",
              "\n",
              "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
              "0           Electronic check          29.85         29.85    No  \n",
              "1               Mailed check          56.95        1889.5    No  \n",
              "2               Mailed check          53.85        108.15   Yes  \n",
              "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
              "4           Electronic check          70.70        151.65   Yes  \n",
              "\n",
              "[5 rows x 21 columns]"
            ]
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      "cell_type": "code",
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        "id": "yQJYcrjpy0pN",
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          "height": 466
        },
        "outputId": "3d40f56b-d466-430c-be0d-acb578561d59"
      },
      "source": [
        "# See if the data is usable.\n",
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 21 columns):\n",
            "customerID          7043 non-null object\n",
            "gender              7043 non-null object\n",
            "SeniorCitizen       7043 non-null int64\n",
            "Partner             7043 non-null object\n",
            "Dependents          7043 non-null object\n",
            "tenure              7043 non-null int64\n",
            "PhoneService        7043 non-null object\n",
            "MultipleLines       7043 non-null object\n",
            "InternetService     7043 non-null object\n",
            "OnlineSecurity      7043 non-null object\n",
            "OnlineBackup        7043 non-null object\n",
            "DeviceProtection    7043 non-null object\n",
            "TechSupport         7043 non-null object\n",
            "StreamingTV         7043 non-null object\n",
            "StreamingMovies     7043 non-null object\n",
            "Contract            7043 non-null object\n",
            "PaperlessBilling    7043 non-null object\n",
            "PaymentMethod       7043 non-null object\n",
            "MonthlyCharges      7043 non-null float64\n",
            "TotalCharges        7043 non-null object\n",
            "Churn               7043 non-null object\n",
            "dtypes: float64(1), int64(2), object(18)\n",
            "memory usage: 1.1+ MB\n"
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        },
        "outputId": "6abffb7a-7aa3-4c9f-c962-dbbed99caa74"
      },
      "source": [
        "# Analyze if there is non-numeric data in the 'TotalCharges' column since it's showing as an object instead of float64.\n",
        "data['TotalCharges'] = pd.to_numeric(data['TotalCharges'], errors = 'coerce')\n",
        "data.loc[data['TotalCharges'].isna()==True]"
      ],
      "execution_count": null,
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              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>...</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>488</th>\n",
              "      <td>4472-LVYGI</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Two year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>52.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>753</th>\n",
              "      <td>3115-CZMZD</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>...</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>20.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>936</th>\n",
              "      <td>5709-LVOEQ</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>80.85</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1082</th>\n",
              "      <td>4367-NUYAO</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>...</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>25.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1340</th>\n",
              "      <td>1371-DWPAZ</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>56.05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3331</th>\n",
              "      <td>7644-OMVMY</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>...</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>19.85</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3826</th>\n",
              "      <td>3213-VVOLG</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>...</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>25.35</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4380</th>\n",
              "      <td>2520-SGTTA</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>...</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>20.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5218</th>\n",
              "      <td>2923-ARZLG</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>...</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>One year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>19.70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6670</th>\n",
              "      <td>4075-WKNIU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>73.35</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6754</th>\n",
              "      <td>2775-SEFEE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Two year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>61.90</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
              "488   4472-LVYGI  Female              0     Yes        Yes       0   \n",
              "753   3115-CZMZD    Male              0      No        Yes       0   \n",
              "936   5709-LVOEQ  Female              0     Yes        Yes       0   \n",
              "1082  4367-NUYAO    Male              0     Yes        Yes       0   \n",
              "1340  1371-DWPAZ  Female              0     Yes        Yes       0   \n",
              "3331  7644-OMVMY    Male              0     Yes        Yes       0   \n",
              "3826  3213-VVOLG    Male              0     Yes        Yes       0   \n",
              "4380  2520-SGTTA  Female              0     Yes        Yes       0   \n",
              "5218  2923-ARZLG    Male              0     Yes        Yes       0   \n",
              "6670  4075-WKNIU  Female              0     Yes        Yes       0   \n",
              "6754  2775-SEFEE    Male              0      No        Yes       0   \n",
              "\n",
              "     PhoneService     MultipleLines InternetService       OnlineSecurity  \\\n",
              "488            No  No phone service             DSL                  Yes   \n",
              "753           Yes                No              No  No internet service   \n",
              "936           Yes                No             DSL                  Yes   \n",
              "1082          Yes               Yes              No  No internet service   \n",
              "1340           No  No phone service             DSL                  Yes   \n",
              "3331          Yes                No              No  No internet service   \n",
              "3826          Yes               Yes              No  No internet service   \n",
              "4380          Yes                No              No  No internet service   \n",
              "5218          Yes                No              No  No internet service   \n",
              "6670          Yes               Yes             DSL                   No   \n",
              "6754          Yes               Yes             DSL                  Yes   \n",
              "\n",
              "      ...       DeviceProtection          TechSupport          StreamingTV  \\\n",
              "488   ...                    Yes                  Yes                  Yes   \n",
              "753   ...    No internet service  No internet service  No internet service   \n",
              "936   ...                    Yes                   No                  Yes   \n",
              "1082  ...    No internet service  No internet service  No internet service   \n",
              "1340  ...                    Yes                  Yes                  Yes   \n",
              "3331  ...    No internet service  No internet service  No internet service   \n",
              "3826  ...    No internet service  No internet service  No internet service   \n",
              "4380  ...    No internet service  No internet service  No internet service   \n",
              "5218  ...    No internet service  No internet service  No internet service   \n",
              "6670  ...                    Yes                  Yes                  Yes   \n",
              "6754  ...                     No                  Yes                   No   \n",
              "\n",
              "          StreamingMovies  Contract PaperlessBilling  \\\n",
              "488                    No  Two year              Yes   \n",
              "753   No internet service  Two year               No   \n",
              "936                   Yes  Two year               No   \n",
              "1082  No internet service  Two year               No   \n",
              "1340                   No  Two year               No   \n",
              "3331  No internet service  Two year               No   \n",
              "3826  No internet service  Two year               No   \n",
              "4380  No internet service  Two year               No   \n",
              "5218  No internet service  One year              Yes   \n",
              "6670                   No  Two year               No   \n",
              "6754                   No  Two year              Yes   \n",
              "\n",
              "                  PaymentMethod MonthlyCharges  TotalCharges  Churn  \n",
              "488   Bank transfer (automatic)          52.55           NaN     No  \n",
              "753                Mailed check          20.25           NaN     No  \n",
              "936                Mailed check          80.85           NaN     No  \n",
              "1082               Mailed check          25.75           NaN     No  \n",
              "1340    Credit card (automatic)          56.05           NaN     No  \n",
              "3331               Mailed check          19.85           NaN     No  \n",
              "3826               Mailed check          25.35           NaN     No  \n",
              "4380               Mailed check          20.00           NaN     No  \n",
              "5218               Mailed check          19.70           NaN     No  \n",
              "6670               Mailed check          73.35           NaN     No  \n",
              "6754  Bank transfer (automatic)          61.90           NaN     No  \n",
              "\n",
              "[11 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dq4060fjmT-C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "74a51a51-f576-44c8-d82d-26b008f04781"
      },
      "source": [
        "# Above we see that the blank \"TotalCharges\" happen when customers have 0 months tenure so we will change those values to $0.\n",
        "data[data['TotalCharges'].isna()==True] = 0\n",
        "data['OnlineBackup'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Yes', 'No', 'No internet service', 0], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2E-8kDo0UxF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "92e512ba-22c0-44ba-9fa6-20b481fb194d"
      },
      "source": [
        "# See how many rows and columns.\n",
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7043, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CxUAu1yBm02V"
      },
      "source": [
        "More data cleanup: next we’ll convert the categorical values into numeric values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6etPuXjm_BL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "cb79c0ee-9d89-430d-df06-e000dcf8a689"
      },
      "source": [
        "data['gender'].replace(['Male','Female'],[0,1],inplace=True)\n",
        "data['Partner'].replace(['Yes','No'],[1,0],inplace=True)\n",
        "data['Dependents'].replace(['Yes','No'],[1,0],inplace=True)\n",
        "data['PhoneService'].replace(['Yes','No'],[1,0],inplace=True)\n",
        "data['MultipleLines'].replace(['No phone service','No', 'Yes'],[0,0,1],inplace=True)\n",
        "data['InternetService'].replace(['No','DSL','Fiber optic'],[0,1,2],inplace=True)\n",
        "data['OnlineSecurity'].replace(['No','Yes','No internet service'],[0,1,0],inplace=True)\n",
        "data['OnlineBackup'].replace(['No','Yes','No internet service'],[0,1,0],inplace=True)\n",
        "data['DeviceProtection'].replace(['No','Yes','No internet service'],[0,1,0],inplace=True)\n",
        "data['TechSupport'].replace(['No','Yes','No internet service'],[0,1,0],inplace=True)\n",
        "data['StreamingTV'].replace(['No','Yes','No internet service'],[0,1,0],inplace=True)\n",
        "data['StreamingMovies'].replace(['No','Yes','No internet service'],[0,1,0],inplace=True)\n",
        "data['Contract'].replace(['Month-to-month', 'One year', 'Two year'],[0,1,2],inplace=True)\n",
        "data['PaperlessBilling'].replace(['Yes','No'],[1,0],inplace=True)\n",
        "data['PaymentMethod'].replace(['Electronic check', 'Mailed check', 'Bank transfer (automatic)','Credit card (automatic)'],[0,1,2,3],inplace=True)\n",
        "data['Churn'].replace(['Yes','No'],[1,0],inplace=True)\n",
        " \n",
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 21 columns):\n",
            "customerID          7043 non-null object\n",
            "gender              7043 non-null int64\n",
            "SeniorCitizen       7043 non-null int64\n",
            "Partner             7043 non-null int64\n",
            "Dependents          7043 non-null int64\n",
            "tenure              7043 non-null int64\n",
            "PhoneService        7043 non-null int64\n",
            "MultipleLines       7043 non-null int64\n",
            "InternetService     7043 non-null int64\n",
            "OnlineSecurity      7043 non-null int64\n",
            "OnlineBackup        7043 non-null int64\n",
            "DeviceProtection    7043 non-null int64\n",
            "TechSupport         7043 non-null int64\n",
            "StreamingTV         7043 non-null int64\n",
            "StreamingMovies     7043 non-null int64\n",
            "Contract            7043 non-null int64\n",
            "PaperlessBilling    7043 non-null int64\n",
            "PaymentMethod       7043 non-null int64\n",
            "MonthlyCharges      7043 non-null float64\n",
            "TotalCharges        7043 non-null float64\n",
            "Churn               7043 non-null int64\n",
            "dtypes: float64(2), int64(18), object(1)\n",
            "memory usage: 1.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LKxD7MsXnYjV"
      },
      "source": [
        "Let's look at relationships between customer data and churn using correlation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZh4954qnm0y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 931
        },
        "outputId": "6f48ffa5-c5ad-493a-de78-168088a9a87e"
      },
      "source": [
        "corr = data.corr()\n",
        "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values, annot = True, annot_kws={'size':12})\n",
        "heat_map=plt.gcf()\n",
        "heat_map.set_size_inches(20,15)\n",
        "plt.xticks(fontsize=10)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ONvu86Z/L+DmlpKTQtUsXbty4wTcDB1KtWjWz14mJiWHYsGEM9fXFwcHhsZnTZ7LNJtND\n+kR95nq2t8t0UJYdTdMYN348TpUq0aFD+xxny4mnkd8Silo9Z91W7dHrE9PK6BOxtX10//IoYeEH\n0TRomINrIOVEftbx2bNnObD/AMHLlnL02LG8ZcznPjmjZ555Jk99cl7r9VGZmzZpyq8rV9KsaVNi\nY2PZvfsP0zWxOnbsSIsWzWnUsCGHDx+m/4Cv+WV5MJ6engWW962ePdnz55/4vNSW5ORk2r70Ei1f\nfNFU9tixYwwe6ktqaiqTJozPlCMnilSf9pTbcnr37t1j0KBBfNq7t+mX+Vzn/w/3F3FxcURERLBg\nwQIcHBwY+M03/PDDD/Q3Xv8NYMWKFbzcuTMlSpR44vyFfT9SUO04z8f2RahPFv9N/+YZLR8Cp1RV\nfRG4A1gBs4Auqqq+BFwD3jSWbQz0ApoBXxn/Nh54X1XV9kAFAEVRWgKVVVVtBbwEjFQU5eG3udh/\n+yALGH4BvJ+UZPa3xMREHNN9qX1UmYzPJSUloWma6UtxcnIyI0eNIubaNaZNnYq1tTUAvXr1ol3b\ntlhZWVGtWjU6derE3r17C3Xm8ePGceToUV7v0oUdO3bw/PPPZ3lh2PR++WUF3bp2oVvXLpw8cYL7\nSWlfAh6u1zHDAIIhn/mXBUN2R+wzPJeYmIiDgyMAQ4cOJS4uju7duvLDooU0b97cLF/79u35fddu\nhg0fzueffcrNmzcz512xgq5dutC1SxdOnDhh9qUlYz2lz5vxy01iYiIOjo6Znnv494esra3ZtHkz\nv23dytatW1m1cqXpuYiICD7t3ZtPevfmFeNFy3LKwcGB+1lkcky37mzrOV2Z7BjayGhiYq4xbeoU\nUxuxlPzObylFrZ4dHOyzbKuOjhn6jvtZ9B05rNfftm2jc6cOecqZXn7VsaZpBI0fj+/QIRQvXjzv\nGfOxT87Ok/TJZnnyUK8Z24kps6Mjn3/+GZUqVqJHz7cYGzSOFi1amHJ98/UAGhm/dNSvX5+GDRpw\nICSkQPNOnzETN1c39v65h71/7kGv1/PjTz+ZytarV48d27YyZ/ZshvgOQ1XPPDavpfM/LQXVlsFw\ncdZPP/uMF198kU8/fbKZDP/1/qJEiRK0adOG8uXK4ejgwJs9e7L/wAGz19m6dSudO3fOQ/7Cvx8p\nqHac52P7ItQn/9tYW1kV+kdh8G8eaKkF7DP+/0bACagOrFUUZQ/QBng4n+ywqqo6VVXj0y3vparq\nwyH4h/PCmgNNjctvx1B/D39CCMuPN1HYeHt5mU2fi4uL4969e3imO03D28uLqOho0781TSMqKooq\nVatmWj4yMpKKFSuaRrz9AwJISkxk5owZpquQp6SkZDqPPiU5Ocd3EymIzGCYOjl/3jw2bdzI6NGj\niY6OpnqGGRgZvf32O6xbv4F16zfw5ps9iYqKNFtvhYoVKZnh1wEvL2+zcmnvzxPvDM9FRl6iinEa\nd7ly5Zk6dRobNm5i4qTJ3Lhxg2rVqhMTE8Mfu3eblmncuAlOTk6mU3LM8r7zDus3bGD9hg282bMn\nUZHmedPXkymvt7dZuYd5K3t6Znou8lJa3s2bNplOQypXrhydOnZk3/79gOEODP369mXA11/TvXv3\nR9ZxVrJtI+l+ofD28s6ijUSb8j2Kf2AgSUlJzJwx3ayNWEp+5y8qOS1dz5nzxmfO6+1FVHTa9YA0\nTSMqOooqVbxztI69e/9HyxYvPr5gDuVXHcfExHDmzFm+HTKUl9q15/8GfcvRY8fo8QR3OsnvPjk7\nT9InPzZzDuvV2zuLzBUqUKpkSRwdHPD3G8OmDeuZ891sdHod1atV4/79+5zLcJ2hlJSUHO378jPv\ngZAQOnbsQPHixXFwcMCndWsOHTrM3bt32fLbb6ZlFKUGz9WtS/gT3MWsyPdp+dyW4+Pj6duvH6+9\n+ir9+vWzfP7/SH/h4uJCfHzaob91sWJmA/QRERHo9Hpq5vKOkY/OX/j2IwXRjvPt2L6Q9sniv+nf\nPNBiheF0IAANw6k9l1VV9TE+GqmqOsn4fHIWy6em+/+H9XQf+CHda9RSVfVCuuf+9Ro1asTVq1c5\nfOQIYLgVb6tWrcxGvatWrUrZsmX5zXjAtXHjRlxcXPCqXBkfHx/CwsJM1934eelSOnXqBMDvu3Zx\n4cIFxo8fn+kXkK8GDGCH8aKoMTEx7Nq9m5YtWxbqzAO+/prffzdcWGvT5s04Ozvn6jzljOtdtvRn\n03qzen9Hjhiu1xO8bBktW7XCwcGR9h06sG3bNm7dukVycjLLly+nU2fDa0wYP45lS5cCcDA8nOvX\nr/PCCy8YLoY7ejTnz50DDOflPtyZ5ibv0p8fk/ewIe/Dz8PB0ZEOWeTtbHyNDRs2EBwcDMCDBw/Y\nv38/NaobTnUaFxTEe++/bzrVLLcaNWpo3kaCg2nVsmWGNlLF0Ea2bgVg46ZNuLg44/WYa8EY2shF\nxo8LyvMvewWRv6jkzI96btSwIVevxnD4yFEAlgYH06rli+Z5q1ShbNkybNm6zZh3My7OLjmq11ux\nscTevk3lypabcpxfdezi4sL+/+1l9+872f37TqZNncLz9eqxOt2sspxnzL8++VHy0ifntV4NmcPT\nMi9bZsq8eMmPptMzz5+/QGhoGD5tfEhMTOTDj3pxzHjaxdmzZzl67BhNmjQp0LxelSvz11+GX51T\nUlLYt38/VatVxcbGhvETJhIaZvjd6VZsLMdPnKB69bRTUnOq6PRpBdOWv5szh8aNGvHBBx/kMf9/\nu7/o2KED27dv59q1a6SkpLBu/Xqz7UtVVby8vEzXHXoSRWE/UlDtOG/H9kWrTxb/TVaaphV0hnyh\nKMq3QAVVVX0VRfkUGAkkAd1UVT2lKMpXGGaqlAP6q6raw7jcTVVVKyiKcgh4FzgDbAMOADuAKcCL\ngC0wWVXVr4zXaFmtqurmR2XqY+Vl0couWakCg/78FQDnmlW5fi6C1OQUZrR9lztXrllkHTN0pzP9\nLTw8nEmTJ6PX6/Hw8CAwIICUlBT69uvH2jVrAEPn4x8QwN07dyhXvjx+Y8aYLlS5fft25s6bR0py\nMjVr1cLfzw9HR0f69O2LqqpmI+D16tUjwN+fEydOMGHiRO7du4eNjQ3vv/8+3bs9+grsBZ35yJEj\nBBh/YXdxcSFo7FicnZ0zZUsl+x34ju3bmTdvLsnJKdSqVZMxfv44Ojpy4vhxvv9+Dt/PnQcYBkom\nTZ5EovH9+QcEmi4cu3x5ML/+8guaBk2bNmXI0KHY2Nhw8eJFRowYTty9e5QsVQp//wDTAfHOHTtY\nuHABDx48wMrKil69PuZ14906ALI75ti+fTvz5s4lOSWFWjVr4udvyHv8+HG+nzOHufPmmT6PyZMm\nmT6PgMB0eYOD+eWXX9Aw5B1qzHvlyhWCgoK4HB1NSkoK9Z5/nhHDhxMXH0+H9u3x9PSkWLG0seOB\nAwfS2nj3C6t0FzXLTvjBg0yaNBl9orGN+PuTkppK335fsnb1KsDYRgIDuXvnrqGNjB6Ft7c3p0+f\nxnf4CJKTk4mOjsbLywuADevW0qdfP1T1TIY28hwBfn6PzZQb+ZXf0gpTPVtpqdk+lz7vxMlTTW11\nrP8YUlJT6fNlf9atMnxpOHP2HP6BY7l79y7ly5fDb9QovL29OHX6H3xHPMx7GS8vw0HzxrWGPufU\n6X/o//XX7N6xPcf1p1k9/veRp9EWwg8eZN68+fywaOGjw2STN7/65JWrVhEcHEx8fDzx8fE4OztT\np04dgsaOzXGfTDbtIi/1CrB9xw5j5hRq1qqJ/5gxODo6cuvWLYb4DuPq1avY2dkxfJivaWr6vn37\nmDFzFkn372Nvb0+/vn3wad360XWez3ljYmIYO24clyIuAVCnTh1GDB9GiRIlCAsPZ8aMmSTodKSm\nptKta1c++bhXjvJaMv+j2vHKVasJXr7cvI08+yxBYwMfHagQteWGjRpRsWJFs2tUvPfee/R8803z\ncDns4/6r/QXAypUrWbJkCTY2NrxQvz6+vr6mL+s/L13KyZMnmThhQvaRU7P6rTZD/kK0H9GKZT37\noiDacY6P7Ytgn2zv+EzhOHcln4yyr1roBxACE88X+Gfwbx5oqQBswHDh253AJ8BHwFQMs0+uYLiO\nSzOyHmjpBEwGLmG4IG60qqp+xovhtsMwY+Z7VVV/LKiBlqchq4EWYVmPGmgprArJqY85lpOBFvHf\nk5OBlsImJwMthUpRyws5+nIq/oOkLee/IljHORloKUyyG2gp1IpaO0YGWgoDGWjJR4qiVAZqqqq6\nXVGUZoC/qqqWu+LgE5CBFpEVGWjJfzLQIrIiAy1PQVHLC0XyoF48BdKW818RrGMZaHkKilo7RgZa\nCoPCMNBSBLe2HLsL/J+iKKMxzD4ZUMB5hBBCCCGEEEKIIquw3NWnsPvXDrSoqnoH6FjQOYQQQggh\nhBBCCPHfUfTm6AkhhBBCCCGEEEIUUv/aGS1CCCGEEEIIIYSwHJmpkTNST0IIIYQQQgghhBAWIgMt\nQgghhBBCCCGEEBYipw4JIYQQQgghhBDiseSuQzkjM1qEEEIIIYQQQgghLEQGWoQQQgghhBBCCCEs\nRAZahBBCCCGEEEIIISxErtEihBBCCCGEEEKIx7KWS7TkiMxoEUIIIYQQQgghhLAQGWgRQgghhBBC\nCCGEsBA5dUgIIYQQQgghhBCPJbd3zhmZ0SKEEEIIIYQQQghhITKj5SmaoTtd0BFy7RvHWgUdIVdm\nxh8v6Ai5duZOSkFHyLVaZawLOkKuaFZFb0zZJjaioCPkinb1fEFHyDVd7fYFHSHXHJLuFnSE3CmC\n216cdYmCjpBrpXTXCjpCrlilJhd0hFyLsHEu6Ai55q27UNARcuV66WoFHSHXKl05VNARciW1fOWC\njpBrxe7HF3SE3HOsW9AJRCEgAy1CCCGEEEIIIYR4LLnrUM4UvZ+ahBBCCCGEEEIIIQopGWgRQggh\nhBBCCCGEsBA5dUgIIYQQQgghhBCPJXcdyhmZ0SKEEEIIIYQQQghhITLQIoQQQgghhBBCCGEhcuqQ\nEEIIIYQQQgghHkvuOpQzMqNFCCGEEEIIIYQQwkJkoEUIIYQQQgghhBDCQmSgRQghhBBCCCGEEMJC\n5BotQgghhBBCCCGEeCy5vXPOyIwWIYQQQgghhBBCCAuRgRYhhBBCCCGEEEIIC5FThwqB0LAwpk2b\nhk6nw9XFhYCAAJycnMzKqKpKUFAQt+/coWyZMowcOZIaNWoAsHXbNhYuXEhycjLVqlXD38+PkiVL\nkpyczOQpUwgJCSE1NZXGjRszzNcXGxsbevfuTfTly9jb25vWsWD+/EzrzQ/FbGzoNmEo7Qd9hq97\nU+5cjsn3daYXGhbO1Bkz0Ov0uLg4E+A3BueM9X3mDGPHTeDOnTuUKVOGUcOHUaNGdQB0Oh0BQePY\nvmMnR8JDTcvodDqCJkzk7+PHsS5mzYstmjPw6wFYW1tb/D3s272DtcuWkJKcjId3FfoOHoVjiRKZ\nyh3c9xcrf1zAgwcPKFmqFJ8O9MXTuyoAd2/HMnvcGK5fvcKsZWssnjG/6tl3xEhOnT5t+nd8fDz1\nnnuO6VMmWzh/GNOmz0jbLv39stguzxA0blzadjliuGm71Ol0BI4NYvuOHRw+GG7RbFkJOXKcKfN+\nRKdPxNWpImOH9Me5YgWzMpqmsWTlBmb+EMziqQE0qFsr0+sEr/+NcbMXcXLX2nzPHHryHFOWb0aX\nlIRLhbKM/awnzuXLmJU5fOYik4M3Ea9PwsG2OEPef52GNasAcP7yNUYu+JXbcQmUKfEMQV+8RVU3\ny/dhO7ZvY/EiQx9btWo1Ro3xo0TJkpnKhYeFMWuGoS93cXFhlF9aX75g7vfs3LmD1NRUFKUmw0aO\npGTJUty8eZMJQWOJiLiIdTFrXn7tNT7q9XGe8oYePMzUWd+j0+txcXYicJQvzpUqmZVRz54jcNI0\n7ty5S5kypRk1ZBBKdUPfcP5iBKMCJ3D77l3KlC7F2FHDqOrtxdDRgZxSz5heIz4+nufr1mH6hMA8\n5c2c/xBTZ81BZ+w7AkcOw9kpQ/4z5wicNDWt7xg6CKV6NUP+CxcZNXY8t+/cpUzp0ob8VbwtmjEr\nO7dv48cfFpGSnEyVqlUZPsaPEiUyt5OD4WF8N2M6er0OZ2cXRozxp5KxnRw7eoRJ44JISkrC2cWF\nMYFjqVixUqbXyKvQQ0eZ8v2bPVagAAAgAElEQVRCdPpEXJwrMdb3/3CuVNGsjKZpLPllNbMW/Mji\nmROp/1wd03M6nR7/KbPY9sefHPvjN4vny0rI4WNMmbsYnV6Pq1Mlxg79BudKWfRxv65l5sKfWTx9\nHA2ee9b03KrN21i6eiOpKam4OlciYPCATMvnhz9/386vP/1AcnIylatU5ZthY3gmi/11cnIyP86d\nzbpfg/lp7RYqVMrcly36bgb79uxiyepN+ZI15MgJJi9catiPVKpA0Lf9cK5Y3qyMpmksXrWJmUtW\nsGTyGBrUqWl67tylaEZM+Z479+IoU6oEQd9+SbXK7hbPuWvHdpYuMWxr3lWqMmTUmCy3tcMHw5g7\nawZ6nQ4nFxeGjvSjkpMTycnJLJgziwP7/sf9pES69XiLtz/4CIDr12KYOj6Iq1cvo2nwRs+36dqj\np8Wyh/z9D5N/Wo1On4RrpfIE9f8I5wplzXOfPsfEJatI0CVib2eL7ydv0vBZw7HFqh17+XnzLlJT\nU3GrVJ6Afh9mWj4/5bXveFpCjhxn8vyfTcdEQYO/zLotr9zIzMXLWTLFz3RMFHb0BH1GjMMlXf/Q\ntkUT/u/T957qeyjM5PbOOSMzWgqYTq9n6NCh+I0Zw6aNG2nVujWBY8dmKjfU15devXqxaeNGPvnk\nE4YNHw7A1atXmThxInO++46NGzbg6urK7O++AyA4OJiIiAhWr1rF2jVrOHfuHBs2bDC95tjAQDas\nX296PI1BFoB+GxaSFK97KuvKSKfXM2TYcPxGjWLT+rW0btWKsUHjM5Ub4jucjz/6kE3r1/LJxx/h\nO3Kk6bkPen2Ci7NLpmUWLV7CgwcP2LBmNStXBHPy1GnWb7T8wdDNazEsmT2VYeOnM+PnVVR0duWX\nH+ZmKhd74zrfTwxgwIgApv/4Ky3admThtAkAxN+7i9/AvqZBF0vLz3qeEDSWjWvXmB41FYUur71m\n8fxDfYfhN3oUmzasp1WrVgQGBWUqN3SYL716fcSmDev55OOPGTYiLf+HvXrh4uJs0VzZ501k8Nip\nBAzqx28/z8GnWSMCps/PVC5gxnwioq9QrkzpLF/nxq1YVm/emd9xAdAl3mfwnGX4f9qDLVOG4vNC\nbQKWmA/u3H+QzIBpPzLwrZfZNGkw/Xt0ZMicYABSUlP5ZsZPfPJqG7ZNG8Z7HVqw5o/QrFaVJzFX\nrzJl4kRmzPqO1es24OLqytw532Uqp9frGTlsKCNGjWHN+o20bNWaCUGGvnz7tq2EhoawdPkvrFq7\nntTUVJb88AMAM6dPxbNyZVav28APP/3MxvXrCAsNeeK8Or2eIaP88Rs+hM2rgvF5sTmBE6dlKjd4\npD8fv/8Om1cF0/uD9/D1MwyWpKSk8I3vKD754F22rlnBu2++wZoNmwGYGDCKTb8uNT1q1qhOl1c6\nPXHWbPOP9MNv+FA2r16Bz4stCJw4JXP+UX58/ME7bF69gt4fvofvmPT5R/LJB++xde2vvNvzDdZs\n3GzRjFmJibnK9MkTmTprNr+sXY+zqyvz58zJVE6v1zN6uC/DRo3m17UbaNGqFZPGG/qWhPh4RvkO\nYdio0azesIkmTZuxc/s2i2fV6RMZ7D8e/yHfsGX5D/g0b0LA1NmZygVMnc2lqMuUK1sm03Pv9RuI\ni7PlB4Cyo9MnMjhgEgGDv+K3ZQvwad6YgGmZ6zdg2hwioi5Trqx5H3f8nzPMWbKcH6aOZfPSedSo\n4sW0BUvyPff1mBjmzZiM3+RZLFixFidnV35ekDk3QKDv/2Hv6Jjta104e4aQvXvyKamhjr8dN4PA\ngV+wdclMfJo2wH/Wwkzl/Gct5NLlK5QrU8rs7ykpqXztP4VP3+rC9p9m816XzqzZusviOa/FXGXW\n1IlMnD6LpavW4ezqyqK5WW9rASOHMXj4KJatXk/zF1sxbaJhW9uyYR2nTpxg0dIV/BD8K79t3sDf\nRw4DMDkogIZNmvLzr2uZOvt7Fs79josXzlskuy4xiW+nLiKw34ds/T4Qn4bP4T8v2KzM/QcP6D/+\ne/7vg+5s/s6fr959nW+nGfYXx89G8N0vm1jsN5At3wVQ3dONqUvz/0cRU34L9B1Pg06fyLdB0wkc\n1JetP83Gp1kD/GdkPibyn7mAS9kcE9VVqrFlySzTQwZZxJN4agMtiqJ8qShKiKIofyqKEqYoSrtc\nLu+rKEqzXJTvpCjKAUVR9iuKckhRlH7p/t7X+P89jP99XlEU/9zksZSwsDDc3d2pVcswitqta1cO\nHDhAQkKCqczZs2eJi4vjpZdeAsDHx4fY2FguXLjAH3v20LhxY1xcXEzL79xp+KJUv359fIcOpXjx\n4hQvXpw6depw/rxldhZ5sSVwNpv9phfIusPCwnF3c6N2LcMvMN26vM7+kBCz+j5z9hxx8XG81MYH\ngDatWxMbe5sLFy4CMHrEcHq80S3Ta589d45GDRpQrFgxbG1teb5ePc6ds3x9h+/7izr1G1LByfAl\n/qXOrxHyV+aDGWsbGwaMDMTdy/Drf8069YiOuGB40sqKwQGTaNC8pcXzQf7Wc3p79+3j/v0H+LRu\nZeH8Ybi7u6XbLrtw4EBIFttlPC+1aQOAj09r03YJMGrECN544w2L5spO6JHjuLs4UbuGYeCsW+eX\n2HfoGAk6vVm5Lh3aEDCoHzY2Wc+yGj9nMV+83yPf8wKEnTqHe8Xy1PY2/NrZvXUj9h8/Q4I+0VQm\nOSWFMb170Li2YaZC/RreXL99j3sJeo6eicDauhjtG9UF4LUXGzDk/dctnvPPP/fQqHFjnI197Otd\nu7Lr98yDUQfDwnBzc6emsc281qUroSGGvty7ShWGDh+Bvb09xYoVo36DhkReugTA+bPnaNS4CQAl\nSpSgVu3anD937onzhh08jLurK7VrGn797Pbay+wPDSchIW1w+8y588TFx9O2tWH7b9OqBbGxd7hw\nMYKjx09gY21NuzaGbeq1zh0Y8k3/TOvZuz+EBw8e4NOyxRNnfXR+5dH54+Joa9zu27R60dB3XIzg\n6N8P87c25u/IkG++smjGrOzds4cGjRrjbBwcfq1LV3bvytxODoUb2olS09BOXn29K2HGdvLXn3uo\nUbMWdeo+B8AHvT7m3fc/tHjWsMNHcXd1obZimD3Y/eWO7A8/TILO/AeQLp3a4T/kmyz7izHfDuDN\n1zpbPFt2Qo8cw93Fmdo1DH1Bt87t2XfwSBaZ2xIweAA21uYTtsuVLs2U0UOoWL4cAPWfe5ZzFyPz\nPXfI//bwfINGVHI27K87vNqF//2R9eDD270+5f3eX2T5XGpqKnOmTuCDz/rmW9bQoycM+5HqhmOG\n7p2y3o90be9DwMA+2NiY1/GRUyrW1ta0f9HQn73erhVD+3xk8Zz7/vqT+g0b42Tc1l5+rSt/7vo9\nU7kjB8NwcXWjhnFb6/xaFw6GhqBLSOBgWAjtOnbCzs6OEiVK0vnV1/nT+Lm81u0NXuliOPao5OSM\nm7sH0ZGXLJI99Pg/uDtXoHZVTwC6t23OvmOnzPZ7D5JT8O/7Pk3qGvrABrWqcT32DvcSdJQrXYKp\ngz6lYjnDwECD2tU5F3nFItlywhJ9x9MQevQE7s4Z2/LfmdtyBx8CBvUtsJzi3++pDLQoiuIFfAa0\nVFW1NfAeMCo3r6Gq6gRVVQ/kYn3TgTdVVW0OtAY+UhSlvaqq21RVffjzv6/xtY+qqjomN3ks5dKl\nS3i4p02rdHR0pEyZMkRGRZmVcXdzM1vO3d2dixERmZb38PAgNjaWe/fuUbduXby9DdOlk5OTCQkJ\noW7duqayS5cto+dbb/Fmz56sXfv0RsQvhhx+auvK6FJkJB4eGeu7tHl9R2ZR325uXIyIAKBeveey\nfO0mjRuz6489JCYmEhcXT0hoKM2aNrH4e7gaHYmza9p7cHJ15+7t28TH3TMrV7psOZ5vnDY2eTTs\nANVqGaZRlyhZClfPyhbP9lB+1nN6c+fNp8/nn1omdDqXLkXi4e5h+nfW22XkY/LXs3iu7FyKvoKH\na9rsmWccHChTqgSRl6+alXv+WSXb19gbeph4nY5OPpb94pydiJgbeDilTeN1tLejTAlHIq/dMvvb\nw4EUgL1//4OXc0VKPeOAGnkVl/JlGTH/F175diJ9J/9A9PVYi+eMvHQJt3R9rLt7Wh9rVi7yEm4Z\n2nzpMmWIjoqiRg2FGjUMdR8fF8eu33fSsrVhIKBh48b8vnMHycnJ3LhxnVMnTtKgUaMnznspMhp3\nN1ezHGVKlyIyOtq8jKur2XLubi5cvBSJevY8Ls5OjAgYz6tvvke//xtK9BXzdgTw/aIlfPGJ5b9E\nXYqMwt09bbvKOn+U2Xs05Hc15D93zpg/iFd7vEO/gYOJvpL/X0SiIi/hlq7PcHP34HZ27STDPr90\naUM7OXf2DGXKlMH32//jre5dGDVsKHfu3LZ41oioy3i4ps0WdHR0oEypkkRGm9fT83VqZ/saj3ou\nP1yKuoyHW7o+7mHmTH1c5tMhAdxcnGhYL+30hf+FHuS52tn3h5ZyOcp8f+3i5s6d27HEZWgXALXq\nZL/P27phLV5VqlLz2brZlsmriMtX8XBJm9n8jIM9ZUqV5NIV89O7n69dI8vl1QuXcHWqwPDJc+j8\n8df0GTme6KvXLZ4zKsM25Oruzu0s6jQqMtJsm3R0dKRU6TJcjo7CCitSU1NNzzk4OHI52rB/b9Wm\nLY7GmUUnjx8j9tZN6tZ7wSLZI65cx8M57TSbZxzsKVPyGS6lq6dnHOxp36y+6d97D5/Ay9WJUs84\n4lapgukUoofPPVcj/0+LfMgSfcfTEBF9BQ/X9G3ZcEx0KWN/8Yg+4Or1m3w2NJCXew3gG/8pXLt5\nK9uy/0XWVoX/URg8rRktpQF7wBZAVdWzqqq2VhSltqIouxVF2aUoynpFUcooiuJlnPWy2DgTZRGA\noig/KoryqqIoxRVFWWIsE6IoSgfj82cVRZmpKMoIoA8wW1XVaOP64oEOqqruVBSll6IoUxRFGQzU\nUxRlraIoPoqirFYUxVtRlD3GxyFFUVTja3dXFGWfcZ1TjX/rpSjKIkVRNiiK8o+iKL2fpGISExOx\ntbMz+5udnR16fdqoq/4RZTIub2tri5WVldnymqYxbtw4nJyc6NChAwAtW7bk9ddf59dffmH8uHHM\nmj2bgwcPPslbKFISExOxs7U1+5udnT36dL8mJOoTsbV99GeSlbd7vklycjKt27bHp117PDzcafmi\n5b+03k9KpHi691Dc+JknJWaf7/jhcLasXsFH/b6xeJ6s5Gc9PxQWfhBNg4YNGuQ9cAaJiYnYZsqf\ncbvUY2uXoYx9zvNbkj4pCbvi5lns7ezQJSblaPnEpCQmzfuRkQM+z4942azzPrbFzX8Rtbctjj7p\nfpbl1cgrTFq2idGfGGYJxen0HFIv8lbb5myaNJhaXm4Mm7fC8jkTE7F7TB/7sNzj2vPI4b507tge\ndw8PXnnlVQA+/6IPp0+epP1LPrz+cmdeatfONCjzJPRJidhlbJd2dubbXhZl7O3s0OkNg8SHjh7j\nre5d2Gg8PWi4n/lpc2GHDqNpGo3qP//EObPNn2XfkSF/FmUM+fXp8ndl48pgaio1GD4m8+m4lmZo\nJ2mZHraTxAztJCmrvsXejsREPfFxcYSFHKD/198QvHI1tra2zJia+bSpPGdNSsTWtrjZ3+zt7NAn\nJmazRMHTJyVl8ZnbotPnPvPGHbvZG3qIL3vl/6kASRmO0XKyv84o9tZNNqxcTq8++TszKzExCbuM\n7cLWFn0O9yNx8QkcPH6at17twJYfplOrqje+kzKfVpJXSRn62uz65Cy3NWOf3KBJU7ZsXE9cXBx3\n795hx9Yt3L+ftu+5FnOVt7u+ytCBAxgwaAhlylrmGiiJSfexK55VHWez34uIZsLiVfj1zdxWN+4J\nYe/hk/R/27KnTj9KUek7ErPqL3LRliuWL0u7lk2YOGwAGxZNo1KFcvhOsHxbFv9+T2WgRVXVY0AY\ncNE4YNJTURQbYDbwhaqqbYEdwJfGRRoAw4FGwMuKoqQ/ye8dINE4M6Y78PBk+eLAVlVVg4CawNEM\nGe5m+Pdk4K6qqt3T/e2iqqo+qqr6AOeB4YqilABGAi8Z1+mhKMrDb891jRm6Ak+0B3RwcOB+kvmG\nn5iYiKODQ47KZHwuKSkJTdNwMC6fnJzMyFGjiLl2jWlTp5ouzNqrVy/atW2LlZUV1apVo1OnTuzd\nu/dJ3kKR4uBgT9J98x1aYmIijo4Z6vt+FvX9iHOnAabPnIWbmyv/2/MH/9vzB3q9niU//WyR3NvW\nrWLgRz0Z+FFPzv1zigfp3sP9+4bP3N4h63zh//uTuRMDGDpumuk0ovyWn/X80G/bttG5U4e8h82C\nIVtW+R3NyyQ9uszT4mBvT9ID8yz6xCQcHeyzWcLc3KWreLVdKzxdn841ZQAc7Gy5/yDZ7G/6+/dx\ntLfNVPbImQj6TV6M/6c9aFzbcHpUCUd7anq68lw1T4oVK8ZHnVtx9OwldNkcsObGyl9+4c3uXXmz\ne1dOnTxBUhZ9bPq2DGCfbXtOKzd23AR+/+NPHBwcGD1yBAABfmNo07Ytu//cy7bfd3MwPIydO7Y/\ncXYHe3uSMrXLJPNtz94h2zIlSjxDzerVeK5ObUO9vtOTo8dPoEv3Jea37b/TuX3bJ874yPxZ9h1J\nmfqO7PoXQ/7qPFfnWWP+tzLlt5TVv/7C22904+03unHq5EmzOjXtizP0B/b2WfctDg6OPFOiBA0a\nNcHdwxMbm+L0fOddwkJyNJE3Vxzs7bl//4HZ3/RJSWbHHYWNg33mdmHo43KXecX6LXz/4woWTx9H\nxfL5cwHRTWt+5Yt33+CLd9/gzOmTZsdo95Mevb/OysJZ03jn488oWarU4wvngaGOs2oXOduPlHjG\nkZpVvahXq7ph23vjVY6cOvNEg2EZrV31Cx/07M4HPbtz+tRJs742220tm/24g6Mjr7zelYaNm9Dv\nkw8Z7TuYho2bml1M18nZhV/Wb2bhz8tZOHcOIfv+l+f3AIb9XtKDjHV8H0cHu0xlj/xznj5jZxPw\n5Qc0rmM++L5i6x7m/LqZJQEDqVg262uu5Yei0ndk2V8k3c9xW/b2cGPIFx9RrkxpitvY0O+DNwk7\ndtIibVn8tzy1a7SoqvohhlN4jgJDgJ1AY2Choih7gA+Ah/O8zqmqGqOqaipwBcOMmIcaAnuMr3kF\nSFIUpZzxuTDjfzXgiU+4M85OuaOq6hrgWcAT2G7MWR14eM7FAVVVU4DoDBlzzNvLy+x0hLi4OO7d\nu4dn5cpmZaLSTZvWNI2oqCiqVK2aafnIyEgqVqxIKeMO2T8ggKTERGbOmGG6w1BKSgqqqprlSElO\nznS+7b9R5vqON9S3p2daGW8voqIvm/6taRpR0VFUecxdK/aHhNCpQ3uKF7fBwcEen9atOXTYMqdJ\nder2JtN/Wsn0n1bS/vXuxFxOaw8x0VGULV+BZ7K44v7fh8L48btpDJ80i6pK1lOq80N+1vNDe/f+\nj5YtXrRUZDPZbpfp83t5Z7FdRlOlytMZzEqviqcbkenu3hUXn8C9+Hgqu2W+mHBW/tgfTvDaLbTq\n8QmtenwCQKsen2SaZmtJ3q6ViLx20/TvOJ2eewl6PJ3M716gRl5h0OylTPryXVo9n9aGXcuXJS7d\nl+dixQzzRK2L5X2+aM+332bV2vWsWrueN3q8SXS6thAVGUmFChUpWdL8S4+Xl5dZufi4OOLu3cPD\nszLhYWGcP2+47oqdnR1du3Un5MB+AEJDDtCxc2esrKwoXbo0TZo248ihQ0+c3durstl2FRcfz724\nODzTndbk7eVJ1OW0qd6aphEZfZmq3l64OjsTF592LaJi1obDBOtiaYcLf+0PoWXzpk+c8ZH5K1c2\n266yzF/Zk6joR+RPiH9kfkvp8dbb/LJmHb+sWUe3Hm+aTj0AiI6KpEKFCpTMcHeqyhnbSfzDduKJ\ns4sLCfFxadmLFcO6mOWvHeDt6UFkus8/Lj6Be3HxeLq7PWKpglXF093sNCFTH+fu+oilzK3b+jvL\n123m51kTzE61tLTX3niL+cvXMH/5Gl7u2oOr6fbXV6KjKFe+QpZ3LctO+P7/sei7Gbz3eke++exD\nbl6/xnuvdzT7wcUSvD1diUx3mlBcgo578QlUzmFduTpVJD7dtZSsH2571nnf9rq/+TZLV65l6cq1\ndOnew2xbuxwVSfkstjXPyl5m5eLj44iPu2ccyLSh74CBLF21jplzF2JtbU2VatW4f/8+WzauJyUl\nBQAXVzeatXiR8FDLDHh6uzsTme40obgEPffidVR2yXBXtYhoBk5ewOT/+5TWDcxPF1u3ez/Bv+1h\nadC3ZqchPQ1Fpe/w9nAzb8u5PCa6efuO2alCKSmpWFlZYZMPdxEtqqytrAr9ozB4WtdosVIUxV5V\n1dOqqs4AmgDuQAmgjXEWSTNVVQcYF0nO8BLpa0vL8G9b4OGJlg/3Ov9gGMRJn6GyoiiPvY+foig1\ngL7Aw3Ms7gOHHs50UVX1BVVVl2eR84k+0UaNGnH16lUOHzkCwLJly2jVqpXZ6HDVqlUpW7Ysv/1m\nuIXixo0bcXFxwatyZXx8fAgLCyPCeF2In5cupVMnw10gft+1iwsXLjB+/HiKZ5iq+NWAAezYsQOA\nmJgYdu3eTcuW+XNh1MKkUcOGXL0aw+EjhglPS4ODadXyRfP6rlKFsmXLsGWr4W4PGzdtxsXZUN+P\n4lW5Mn/uNfzqkZKSwr79+6lW1fJ39WnUvBUnDodzxXhxts2rltP8pcwzO5ISE5k3KZBBARNwr/z0\nzuGF/K1ngFuxscTevk3lyp6PLftE+Rs1NN8ug4Np1bJlhu2yimG73LrVmH8TLi7OOcpvaY2fr8OV\nazc4dNxw2+uf12yiddOGOf71ZuPimfy1Zgl/rV7MX6sXA/DX6sU5Pih5osy1q3Hl5m0Oq4aLH/+8\ndS+tX6hlNqNF0zRGzP+Vkb260aCm+QBWk2erc/NOHPuOGwaNV+0O5YXqXpmmvudVKx8fwsPDuGTs\nY5cvW0qHTpnvtNOgoaEvP2psM8uDl/Fiy1Y4ODhw7OgRZkydavp1de9ff1KtuuE8+8qVvdj711+A\n4dfWg+FhVKlW7YnzNq7/AldirnH46N8ALF2xitYtmpm3XW8vypYtzZbthou1btiyDRdnJ7w8PWjS\nqD43b91if6jhluSr12/ihefqmE6fuhV7m9jbd/Dy9CA/NG5QnytX0+dfSesWzTP0Hd6GvsOUf6sx\nvydNGjXg5s1b7A8x/O6yet1GXniurtnpX/mhVWsfDoaltZMVwcto1zHrdhITc5VjRw3t5JfgYFq8\n2BIHBwda+bThyOHDnD931vC+1q2lYWPLX+ercf16XLl2ncN/nwDg55Vrad2scY77i4LQ+IXnuBJz\nnUN/nwTg51Xrc5X52o2bzFj4E/Mn+VOpQvnHL2AhTVu25tihMKIjIwBY92swrdt1zNVrrN75F8Eb\ntxO8cTszFv5MhUpOBG/cbnYKsSU0qVeHK9dvcOjEPwD8tGYzPk3q57iOmz5fhxuxt9l38BgAK7f8\nzgvPKplO4cirFq18OBQeTuSlCMN6li+jbYfM29oLDRoSc/Uqfxu3tVUrgmnWwrCt7dz2G/4jfElN\nTeXmjRts27KJdh07Y2trS/CPi9nxm+FOZTqdjqOHDlK1WnWLZG9SR+HKjVgOnTIMvP+06Xd8GtbF\n0T6tf9I0jWGzfmTU5+/QsLb5eq/dus30ZetYMHoAlco9/Tv6FJW+o8nzz3Ll2k3TMZGhLTfIcc7d\n+8IZ4DeZBOOPOUvXbaHpC3UynTYlxONYaZqW7ytRFOVToBXwkaqqmnEGSjhwAZimqupWRVHeBm5g\nOGVntaqqDY3LHgR6AH7AaqAMhovqfqEoigewQ1XVWoqiRAB1VFWNVxTFE8Osl46qqp5VFKUk8Bvg\nj2GAp46qqt8qihKrqmo5RVF8gP7Au8bl+qmqetS4fkcMAzcNVVW9brw70QKgfbrXKQGcUFXV61H1\nkKjXZ1nZ4eHhTJo8Gb1ej4eHB4EBAaSkpNC3Xz/WrlkDGO5w4h8QwN07dyhXvjx+Y8aYLnS7fft2\n5s6bR0pyMjVr1cLfzw9HR0f69O2Lqqqm2S1guEBngL8/J06cYMLEidy7dw8bGxvef/99unfLfIeX\nbxwtOwuiZKUKDPrzVwCca1bl+rkIUpNTmNH2Xe5cuZbn158Zf/yxZcIPHmTi5Kmm+h7rP4aU1FT6\nfNmfdatWAoY74vgHjuXu3buUL18Ov1Gj8Pb24tTpf/AdMYLk5GSioy/j5WX4Ur1x7RpiYmIYO24C\nEca7idSp8ywjh/lSokSJR+Y5fScl1+/zwJ7fWfnjQlJTkvGuXpM+g0dg7+DIudMn+XXJfEZMmsW+\nXduZO2ksFTPcInnM9Lmc/+cUy+bPJikpkTuxt3BycaNchYqMmpr1bSczqlXm8aP6+VXPAKdO/0P/\nr79mdw5PsdCscj+mHH7wIJMmTUafaNwu/f1JSU2lb78vWbt6FWDcLgMDuXvnrmG7HD0Kb29vTp8+\nje/wh/mj8fLyAgxfnHLKJjYiV3nDjp5g/Jwf0Ccm4enmTNCQr0hNSeVz3wA2/DATgC69vyYlJZWo\nKzFULF8Wezs7xvkO4Lma5gdzz7btzsldubtAtnY193fYCjt1ngnLNqBPvI+nU3mCvniLlFSNLyYt\nZP2Ebzl6NoIPA76nsrP5GPnEfu9S29udw+pF/Bev4f6DZFwrlGVM7zfwdHrseLqJrnb7HJXbuWM7\nC+fNIyUlGaVmLUaOMfSxJ08cZ9733zP7e8P11Q8dDGfq5Mkk6vW4e3gw2j+AChUqkJSUxPSpUzgY\nHoamaTg5OTN0+AgqV67M+XPnmDxxPDdu3ABNo2nzFgwc9G22Mwwd7t/N8u/phR86woTps9EnJuLp\n7sbYUb6kpKTS55vBrDUfSwoAACAASURBVFv+I2C4c4//+CncuXuX8uXK4Td8MFWM29nho38TMHEq\n9x88wNXZiTG+3+Jh/MXy1D9n+HLQUP7Ysi5HdceTbHuHjjBh2sy0/KOHG/J/PYh1K35Oyz9uEnfu\n3qN8ubL4jRhqnn/C5LT8w4aY8udEnPWj++zs7Nq5g0Xz09rJsFFjcHR05NSJEyyY9z0zvvvekO/g\nQWZMnYRen4i7hwcjx/hTvoKh3e7ZvYs5s2ZiZQVVqlZj6IiRlC1b7lGrBaCULnf7z7Ajx5gwa56h\njt1cCRo2iJTUVL74dgTrfzLcBrXrR1+QkpJC1OWrVKxQHns7W8YNH4y1dTGGBk7kQXIK0Veu4u1p\nmG20admiHK/fKjXj72k5yfw3479bgF6fhKebC0G+A0lNTeHzwaPZ8KOhbrv06mfs465SsUI57G3t\nGDd8IKGHj7Fg2Sqc0g2yWFsXMy2XExE2TzYLZu+unSxbPJ/UlBSq1qjJ176jcHB0RD11gmWL5hE4\n7Ttux97Ct7/hGlnRkZdwcXPH2tqaoJlzqVAxbbbDtatX8P3qC5as3pSjdXvrLuQqa9ixk4yf+yO6\nxEQquzoT9O2XpKam8tmwIDYunArA658NMrSLq9cM+xFbW8YP6c9zNatx6MQ/+M1cYNj2KlXE/5vP\nc3VK6vXSORtk/uP3HSxZOI+U5BRq1KzJ4BGGbe30yRMsnv89k2cZPtcjhw7y3TTDftzN3QPf0f6U\nL18BvV7POL+RnFUNd0r6tO+XtGln+MHq3JkzzJo6kdu3b5OSkkyLlq3pO2AgxbKZFVfpSniO3x9A\n2AmV8T+sRJeYRGWXigR91ctQx/6z2DhrDEf/Oc/7IyZnmuUyeeCn/O/oSRas3opT+bRBFuti1myc\nlfP7eaSWz9uPQXnpO+o+4QWoi92Pf3yhjDmPnmD890sM9ezqTNAQY1v2HcvGRYY7n77+6UBDzivG\ntmxny/ihX1GnRlWmLFzKrn3hWBcrRtXK7oz8qrdZ//E41h51C8eUinzyY/la+T+AkEe9bp0u8M/g\naQ20WAMTMQy2xGO4nsoEDAMtCzDMSNFjGOgoxaMHWrYB84CqGGazDFNV9a/0Ay3G5ZoBM4yvnQrM\nUFV1laIovUgbINkFlMRwKlN/YA2Ga76k/7b+KtABwzVjkoAjGK7H8hEWGmgpzCw90JLfcjLQUtg8\nyUBLQcvJQEth8iQDLQUttwMtBe1JBloKWk4HWgqTnAy0FCpFcNt70oGWgpTbgZaC9iQDLQXtSQda\nClJuB1oKWk4HWgqT3A60FLS8DrQUhCcZaClo//aBlqUVCv9Aywc3/yMDLcJABlrynwy0PB0y0JL/\nZKAl/8lAy1NQBLc9GWjJfzLQ8nTIQEv+k4GW/CcDLYWPDLTkTNE7AhJCCCGEEEIIIYQopGSgRQgh\nhBBCCCGEEMJC/v338xVCCCGEEEIIIUSeFZbbJxd2MqNFCCGEEEIIIYQQwkJkoEUIIYQQQgghhBDC\nQuTUISGEEEIIIYQQQjyWtZw5lCMyo0UIIYQQQgghhBDCQmSgRQghhBD/z959xzV1/X8cf4U9RHAg\nG1HUq9ZVt63b2tZ+W0W7bG2rtXtop4obce896q57K2jdddQJ7t24ARVHiwpIAiTk90cwEIIVJBTt\n7/N8PHi0uTk3ed/ruSc3555zI4QQQgghrESmDgkhhBBCCCGEEOKx5FeH8kZGtAghhBBCCCGEEEJY\niXS0CCGEEEIIIYQQQliJTB0SQgghhBBCCCHEY8mvDuWNdLQIIYQQQgghhBDi/wVFUcYDDQED8J1a\nrT6U7blvgA8APXBYrVZ//yTvIVOHhBBCCCGEEEII8Z+nKEozoKJarW4EfAJMyvZccaAH0EStVjcG\nqiqK0vBJ3kc6WoQQQgghhBBCCPFYtirVU//3GK2AdQBqtfocUCKzgwUgLfOvmKIodoALkPAk+0k6\nWoQQQgghhBBCCPH/gTdwJ9vjO5nLUKvVWmAQcBmIAaLUavX5J3kTuUeL+EcTk08VdYR8+a5Y9aKO\nkG9DJ79d1BHyTe9Zsqgj5Ivt698WdYT/PH211kUdId9c/75c1BH+81SGjKKOkG/udveLOkL+PWv7\nWfXsXecL/PO3oo6Qb6l1Q4o6Qr54Jlwp6gj5llE6qKgj5EvGyZ1FHSHfrtZ4q6gj5FvFog4g8ss0\nBCZzZEsfoBKQCOxQFKWmWq0+kd8XlY4WIYQQQgghhBBCPJbN46fmPO1ukDmCJZMvEJ/5/1WAy2q1\n+i8ARVH2AHWAfHe0PHuXFIQQQgghhBBCCCHybyvwFoCiKLWBG2q1OinzuatAFUVRnDMf1wUuPMmb\nyIgWIYQQQgghhBBC/Oep1er9iqIcURRlP5ABfKMoShfgvlqtXqsoymhgp6IoOmC/Wq3e8yTvIx0t\nQgghhBBCCCGE+H9BrVaH5lh0IttzvwC/FPQ9pKNFCCGEEEIIIYQQj6Wyfebv0fKvkHu0CCGEEEII\nIYQQQliJdLQIIYQQQgghhBBCWIlMHRJCCCGEEEIIIcRj2cjUoTyRES1CCCGEEEIIIYQQViIdLUII\nIYQQQgghhBBWIlOHhBBCCCGEEEII8VgqWxmrkReyl4QQQgghhBBCCCGsRDpahBBCCCGEEEIIIaxE\npg49BaKioxk3bhwpKSn4+vgQHh6Ol5eXWRm1Ws3QoUO5e+8eJTw86NevH5UqVQJg0+bNzJo1C51O\nR4UKFRgUFoabmxs6nY7RY8Zw8OBBMjIyqF+/Pr1DQ7GzsyM2NpbBQ4ZwMz4eJ2dnwsPDqVK58hPm\nP8TYCRPQpGjw8fEmPGwg3jnznz/PkGEjuHfvHh4eHvTv05tKlSoCkJKSQvjQYWzZuo1jh6JM66Sk\npDB0xEhOnjqFrY0tjV98gR++646tre0T5SwIGzs72o/oReufPiPUvyH3rt/81zM8ZOcXjOuLr6Ny\ncESfeJcHv68g48H9XMvalvLB/Z3vSIycie76ZRwr18WlSVsMKUmmMtqT+9Ce2l+omaMvxzN+yyFS\n0nT4uLsyqH1jvNxdzcocvnKTiVsPk5yahpO9HT+3qU+dIG8A5vxxkg3HL6JCRTlPd3q/3pDSbi5W\nzVjQerxqzVoWL1mKPkOPr48vYQP64e3lRXq6jpFjxhB96BCGDAP169UltGdP7O2t2/wePHaKMTPm\nk6LR4uvlyZCe3+LtWdqsjMFgYN6KCCbOWczcseHUqV7F4nUWr9vIsMmzOfP7GqvkKqz2LT09nVGj\nRhF96JCpfQvt1Qt7e3tu3rzJ4MGDuX7jBgaDgfffe493333XKtvztO7n/0reh5lH/7LAlHloj2/w\n9ixlkXnuikgmzl3CvDFhpszRx0/zZd9h+JTJ2sZWLzbgx087FV7eoycYM30uKRoNvl5lGNLre7zL\n5LKPl69h4qwFzB0/jDo1njM9t3LDZhauiiRDn4GvdxnCe3S3WL9QMs+Yl1Uven2Xe71YvpaJsxcy\nd/xQ6lSvanpuWcRGlqz9DX1GBn7eZRj087f4lPEs/MzP0H6OOneZsSu3kpKahk9JdwZ/HIJ3SXez\nMscuxDJ6xWaSNak4OdjTs+Or1K0UBMCcTXuI3H+ClNQ0WtepSo93XkGlKpxf+ti8eROzM9vZ4AoV\nCAsbhJubm0W56Ogoxme25z4+vgzK1p4nJPxNn959uHHjOpHrN5jWSU5OZviwoZw9e5aMjAxeefVV\nvv76G6tlP3j0pLGN0z5s47o9oi6vY+KcRcwdNzhHXd7EknUb0ev1+Pl4Meinrwu9LmcXdeQ4Y6bO\nIkWjwce7DEN6/4R3jvc3GAzMW7qKSTPnMXfSKGrXqPav5QOIUl9l7NqdaDLrcvgHr+FdorhZmcMX\nYhm3difJ2sy6/GYr6lYMBGDVvuMs3nEYvSED35LuhHVqY7F+Ydi9fQvLF8xBr9NRtnww34UOxLVY\nMYtyOp2O+TMms275Yuav/o3SZYx1evvG9cycNIaSpbLq0/86vMMbb1rnfOJZppJfHcoTGdFSxFI0\nGnr16kXYwIGsj4ykabNmDB4yxKJcr9BQunTpwvrISLp27UrvPn0AiI+PZ+TIkUydMoXIiAh8fX2Z\nPGUKAIsXL+bq1ausWrmSNatXc/HiRSIiIgDo3acPLVu2ZP369Xz//ff07NEDg8HwRPl79u5DWP/+\nrF+3hmZNmzJk6HCLcj1D+/Bx549Yv24NXT/uTGi/fqbnPuzSFR9vH4t1Zs+dR3p6OhGrV7Fi6WLO\nnD3Husj1+c5oDV9HzCI1OaVI3tuMnT1ur3xA8s5V3Fs0ivSrZ3Ft0eERhVW4Nu9ARrZOFYC0y2e4\nt3i06a+wO1k0aemErtzNgHYvEvFdB5pWDmDo+gNmZbTpOnos30nvNxqytnsHPm9ei14rdmEwGDh4\n8QYRRy+w4PPXWdO9PYGlijN+y2GrZixoPT595gzTZ/zCzOnTiFyzmooVKjBh0mQAfl24kISEBNau\nXMGq5UtRn7/A6rVrrZxfS48hYwn/6Ws2LphK80b1CB//i0W58Am/cPXaDUp6uOfyKnDn7wRWbdhm\nxVyF174tWLCAhLt3WbN6NatWruT8+fOsWWPsBAgbNIhGjRqxbu1afpkxg8mTJ3Px4kUrbM/TuZ8f\n5VnLC8bMPw8dz+CfvmLTr5Np3qgOgyZYZh40cSYxj8hcXanAb/Mmmf4Ks5MlRaOlR/gownt0Y+Oi\nmTR/oT7h46ZalAsfN5WrcdcpWcI876k/zzN13hLmjB3ChoUzqFQ+iHEz5xVaXlPmwWMI/7kbGxfO\noHmj+oSPm2aZefx0Y73IkfnY6XPMX7GOhZNH8tuC6ZQvG8DoaXMLP/MztJ9TUtPoOXMVYZ3bsmFo\nd5rXVBi8aINZmbR0Hd2nLuX7N1sTOaQb34a0pNfMVQDsOXWBNXuOsiD0EzYO687ZmBtsOHiyULI+\nbGcnT5nKuohIfH19mTplskU5jSaF0F69GDAwjIjI9TRt1pShQwYDcP/+fT795BMqVKxgsd6UyZOw\nt7dn9Zq1LFm6jE0bN3LwwAGLck/C1Mb9/A0bF0zLbONmWJQLnzAj1zbu2Ok/jXV50nB+WzCN8oH+\njJ5euMdfdikaLT3ChjGo1/f8tnQuzV9sSPiYSRblwsdOIibuGiVLePxr2UwZU9PoOTeSsPfbsH7g\nFzSrVoEhy7aYldGmpfPjrLX06/gKkQM+56s2L9JjTgQGg4HTMfFM/20vM7t3JHLA51T09WTCul2F\nnvv2rZv8MmE0YaMn8cuSNZTx9mXBLMs2A2Bw7x9xds794l2jJi2YsXi16U86WUR+/Cc7WhRFCVIU\nJUlRlF2KouxWFOWgoijt87huU0VRyhR2xoeio6Px9/enShXj1bj2ISEcOHCABw8emMpcuHCBpKQk\nWrZsCUDz5s1JSEjg8uXL7Ny1i/r16+Pj42Naf9s24wlx7dq1TVd47e3tqVatGpcuXSI5OZnTp0/T\nrl07AF584QXs7O1Rq9VPkP8Q/n5+VK1iHA3Tvl1b9h88aJb//IWLJCUn0bJFcwBaNGtGQsJdLl++\nAsCAvn14603Lf54LFy9Sr04dbGxscHBwoFbNmly8eCnfGa3ht8GT2RA2vkjeOzt7/wroE/9Gf+c6\nANpzh7APqAT2jhZlHas1RP/XDTIS//63Y5qJvnwT/xLFqOJrvCId8nxFDly6wYPUdFOZdH0GA9q9\nSFVf41WDBuV9+DtZS5I2jQu37lLVrzRuTg4A1C/vw8Xbd62bsYD1uESJEowcPgzPzKtotWvX4tKl\nywDUrVOb77t1w9bWFkdHR2rVqsnVmBir5o86dgp/Hy+qVgo25m/Tkn1HTvAgRWNWrt3LLQj/6Wvs\n7HIfFTZ86ly++OAtq+UqzPatTp06fNe9e7b9WourV68C8NZbb9Ghg7ED0tvbm4DAQGJiYwu8PU/r\nfv6v5AWIOn4af28vqlYsD0CHV1uy78hJi8whLzcn/KevHpn53xJ17AT+Pt5UrWT8gtm+TWv2HT7G\ngxTzjvl2r7YivEd37GzNR7KVdHdnzICeeJYqCUDtGs9x8UrB6+o/Zz5pXi9ee4l9h49bZn6lJeE/\nf2uZuYQ7w3v/gLub8cpww9o1uRJ3vZAzP1v7OfrcFfw9S1C1rK8xb+Pn2X/mEg+0qaYyOr2egR++\nQf3K5YyZKgZy+14SiSkaDpy9RKvnq+Du6oy9nR0dW9Rn25GzhZJ1166dZu1sSEh7Uztrtk052vOQ\nkPam9lylUjFu/ASaN2tusV7LVq348quvsbGxwdXVlUqVKnHpknXO5SzqcptWmXU5lzbu528s2gvL\nulyDK3E3rJItL6KPHsff14eqinFkbIfXXmH/oaO51OvWDOr1A3Z2//5EhOjzMfiX9qBqoHGEcftG\nNdh/7opZXU7X6xnUqY2pTAMliL+THpCkSaVEMWdGftwWT3fjPq5dIYBL8X8Veu6oPbuoWaceZbyM\nmV7+Xzv27fw917IdO39Kp0++KPRM4v+f/2RHSya1Wq1urlarmwGvARMURXHOw3pdgX+toyUmJoYA\nf3/TYxcXFzw8PIiNizMr4+/nZ7aev78/V65etVg/ICCAhIQEEhMTqV69OuXKGT/AdTodBw8epHr1\n6qahp4aMDNN6zs7OxGV7zzznj40lICBnfnfz/LG55Pfz40rml6KaNWvk+toN6tfn95270Gq1JCUl\nczAqikYNG+Q7ozVcOXi0SN43J1sPTzLuZ+s4SU/DoE3B1t18WL3KxQ3nmo1JObDJ4jXsSvtSvP2X\neHzQE9eWb6NycCrUzDF/38e/ZNYQURdHezycHYlLSDQtc3NyoEUV4xBTg8HAuqMXeL6sF8WdHalb\nzpsTsbe5df8BOn0GO87F0jDY17oZC1iP/Xx9qVuntmn53n37qV7NOGS9Vs2aBAYGAHDnzl/s27ef\nZk0aWzf/tRsE+HqbHrs6O+NRvBix1+PNytV6Tnnka+yJOkpySgqvNn/RerkKsX2rVasWgYHGOnPn\nzh327t1L06ZNAXipVStcXIxXp06cOMGdO3d4vlatgm/PU7qfH+VZywtw9doNAnyzppY9zByTM3PV\nR2eOv/0Xn/UazGtduvP9oDHc+qvwOptj4q4T4JdtH7s441HcLZd9bDkdC8DPx4u6NbOmAeyNOkyN\nf9g2a4i5dp0A36xRpMZ9nFvm3KcTl/Xz5flqxu3RpqayYfsuWr5YuJ/Nz9p+jrn1F/6eJUyPXZwc\n8SjmTOztBLNlL9XJmsKy99RFgrxKUdzFGRWgz3aO5uLoQFy2da2aNSaGAP8A0+Ps7WzOcv7Zyj1s\nz+PiYilevDhBQUG5vn79+g3w9jb+2yUnJ3PixAmqVa9uney5tnH5qcs+PF/N+JyxLv9ByxfrWyVb\nXlyNu0aAX9ax6OLijEfx4sReM+/sqVWtas5V/zUxtxIIKJ01ksbFyQEPV2di72Rd8HJzdqJFTeN0\nX4PBwJoDJ6gd7E9xFyf8SnmYphAB7D1zmepB1j2Hy831uFh8/LLOH3z8/Ll3N4HkpESLslWq5f49\nBODyxfOEdvucz9/rwMQR4TxITi6UvM8aG1vVU//3NPh/cY8WtVqdoChKPFBJUZSpQDqQAbwNFAcW\nAcnAVCAEeE5RlDeBHcA64EXgHvA/wBWYB5TAuP+6qdXqk4qiXAA2ArfVavXQvGbTarU4OJqPRnB0\ndESjyeqN1/xDGa1WS8mSJU3LHRwcUKlUaDQaihc3frk1GAwMGzYMLy8vXn75ZWxtbalevToLFy7k\niy++ICoqikuXLpGalpbX2Gb5HR0ccmRzQqPRZpXRaHFw+OdtzE3Hd95m1+4/aNaqNTqdjlYtW9Ck\n8b9zsv+0Utk5YNDrzJYZdOmo7M3/DVwbtyXl0HYMaVqz5fp7d0i7cgbNsd1gyKDYS+/i0vgNHuxY\nWWiZtel6HHJcRXK0t0WTprMou+3MVUb+dhA3JwfGdDSOcKjiW4o3alXgf+NX4WxvR5nirsz9pI11\nM1qxHq/f8Bv79u1n0a/mw4+7fPIZZ86e5aMPOtGwgXW/lGhSU3HMUQecHB1JyXbF6Z9oU1MZNWM+\nU4f2sWquf6N9+7hrV86cOcNHH35Iw4YNTWXj4+Pp+sknJCUlERYWZvY6T+pp3c+P8qzlffieOY9F\nJwcHNHnM7FmqBC81acCn74bgVsyV0b8sIHTEZOaNCSuEtJn7OGdeRwdSNNpHrPFokVt3sCfqCEum\njbVWvFxptKk4OtibLXNydMhzvXhozIx5rFi/mdrVq9L1vUdNYbWOZ20/a9LSccxxHy5He3s0qbmf\nZ6njbjJq+WZGfvYmAI2eC2bgr5F82LoR7q7OrN5zhNR0y89Ma8hLO/uwnINjzs/Jx5/LPZSenk6f\n3qE0a9acmjVrWiW7sV7kVpfzVy/G/DKfFeu3ULtaFbp2zNMAeKvQalNxyKVea/KZvzBp03W51GU7\nNNlGJT+09eifDF+xDTcXR8Z/ZtkmrI86zb6zl1j080eFlveh1FQt7iWy6rV9Zr3WajQUc8vb/WH8\nAgJp2LgZ7d/7ABsbW8YPHcisyWP5vvfAwoot/mP+yyNaTBRFCQJKYRyp0k2tVrcA9gEPJ24/D3RS\nq9XrgePAx2q1OhYoDyxQq9WNMHas1AC+Bzar1epWwFfAw09qe2BTfjpZwDiSJC3V/ORGq9Xi4uyc\npzI5n0tNTcVgMOCcub5Op6Nf//7cvHWLcWPHmm4kO3zYMI4dP07bdu3YunUrtWrVyvXGZ4/P72TR\nQaPVanFxyZE/LZf8Lv98M9PxEyfh5+fL3l072btrJxqNhnm/Lsh3xv8Sgy4NVY4h0So7BwzpWf8G\n9oGVUDm5kHb+mMX6upsxaKK3Qnoq6NLRHNmJQ1DhXilxtrcjTac3W6ZN1+HiYNnP2/q5ILb37Ejv\n1xvx+bzN/JWUwq4/Y9l7/hrbe77LH33ep02NcvRd/Yd1M1qpHi9bsZIZM2cx+5fplC5tfjO++XNm\nsXPbFi5fuWK6f4vV8js5kZpunl+jTcXFOW+jlaYvXMnrLzUlMNuVQavkKuT2DWDe3Lns+P13436d\nONG03MfHh00bN7Js2TImT57Mnj17Cr49T+l+fpRnLS9kZs5xLGpS0/KcuVyAHz2/6ExJD3fs7ez4\n+sO3iT5x5om+kOdFrnm1qWZ1PC+WrvuNafOXMnf8MDxLlXj8CgVgzGz+JSk/9eKhn7/8mP2RS6hX\nsxqf/tTfmhEtPGv72dnRwaJjRJuWjkuOjgqA4xdj+XrSYsI6t6Ve5jSixtUq0qllAz4bt4BPx/5K\nreAA3FysN/p02bKltA9pR/uQdpw5fZq0bB1AD9vZnPvW2B7n8jn5iHtbZJeSkkL3bt9SsmRJ+ma7\nR19BWa0uf9GF/RGLqFerGp/+/O99iXZ2ciLNor3Lf70uTM4O9rnUZV2udfnl2pXZOaIbfd99mU8m\nLuGv+1mjP5b9cZQZG/cy+7v3Ke1ueUNaa1i/ejlfdnqTLzu9yflzZ0jPds6WllmvnfJQXx+qUr0m\nnT75AhcXV5ycnHj7g485tH9vYUQX/1H/5Y4W5eE9WoBfgI+AW8CwzGXvYex8AbikVqtzG1ucqFar\nH9597BrgDrwAfKkoyi5gWuayh6LzG7JcUJDZMPqkpCQSExMJLFvWrEzctWumxwaDgbi4OMoHB1us\nHxsbi6enp+kqxKDwcFK1WiZOmICTU9YHT0BAAL/MmMH6yEgGDBjAtWvXqFjB8iZm+c+fbMwfmDVM\nsFy5IOKuZc3fNhgMxF2Lo3z5cv/42vsPHuTVl1tjb2+Hs7MTzZs148jRp2MKT1HR372NjXvWF3iV\ngxMqJ2f09+6YljmUr4adpx8lPh5AiY8HYOddFrc2nXFQ6mBTzB2VU9av/ahUNpBh3glibUGe7sT9\nnTVUM0mbRqImjcBSWVcUbt5/wM5zWfctqV/eB6/irpy6doeDF2/wQkU/PFycUKlUvFytHEeu3rJq\nRmvU44jI9SxbvoJ5c2bhn226y85du4iPN/5KVbFixWj3xhvsP3DQqvnLB/oRm+2XsJKSH5CYnEzZ\nbEOS/8nO/YdYvOY3mr7VlaZvdQWg6VtdLaZr5Fdhtm87d+4kPt6Yr1ixYrRt25YD+/eTlpbGmrVr\n0euN9drfz48mTZpwwAo3X3xa9/N/JS8YO0pibzx55r/u3jObKqTXZ6BSqbArpF+rKx/obzZNwZTX\nP+9D49du2s6StRtYMGmE2TSIwvLIzH55y3zy3HlOnP0TADtbWzq2a8PJc+dJLMQh9c/afi7nXdps\nqk9SipbEFA2BXubTfNVxN/lpxgpGffYWTWtUMnuua5vGrB/SjcV9PqNU8WJU9LPerPaOHd9j7boI\n1q6L4O233yEuLut+NbGxsZT29MStuPlV/6CgcmblstrzQP6JTqfjxx9+IDi4AmGDwrGxsd5Xj/IB\nflaoy8b7E9rZ2tKxbeHX5ezKlQ0wmyaUlPyAxKRkAv39/mGtf1c5r1Jm04SSNFoSNVoCy2R1VN68\nm8iOE+dNjxsoQXh5uHHyqnHbIg6cZNnuI8z7oRP+pQvvhr5vvPmu6aa1r4W8Rfz1rHOLG9fiKFmq\nNMXycVH5zq2b3L+bte16vQ7bIrhPjnh2/Zc7Wkz3aFGr1a+o1eoDwERgYuZ9W7L/jMGj5szkHKep\nyizbLfO1m6vV6uyTOfM996ZevXrEx8dz9Jhx9MGiRYto2rSpWW92cHAwJUqUYOPGjQBERkbi4+ND\nUNmyNG/enOjoaNNNIBcsXMirr74KwPbff+fy5csMHz4ce3vzoZXdv/uO7du3A7B+wwa8vb3x9c3/\nnMl6desSH3+To8eOA7Bw8WKaNmlsnr98eUqU8OC3TZuN+ddvwMfbmP+fBJUty+49xp5jvV7Pvv37\nqRAcnO+M/yXp1y5i61YCO58gAJxqNSHt6jnQZV3RebBrDXfnhHF3Xjh354WjuxlD0qZfSVMfwbFa\nI4q1fAtsbEClKx6JkgAAIABJREFUwqnGi6TFnCvUzPXKeRN/P5ljMcbOkcX7z9BECcA523DfdL2e\nAWv3cinzJrcxfycSl5BI+TIlKFvanejLN0xTjfaev0aFMtb9oC5oPb51+zYTp0xh+pTJlPE0/1nG\nnbt2M/2XX8jIyMBgMLBn714q5vLLDAVRv1Y1bty6w5FTxn/LBavX06xh3Txf1YucO5E/Vs/jj1Vz\n+WOV8ddD/lg1N89fbh+lMNu3nbt2MX3GjKz9umcPFStVwsHBgTlz5rB+vfEXylJSUjh8+DAVK5l/\niXkST+t+/q/kBWhQ6zlu3PrLlPnX1Rto3qBOnjPv2HeI7mGjeZA5nWHh2t9o+Hw1HHJML7CW+s/X\n4MbN2xw5eQaABSvX0axR/TznvXXnLybM+pVfRg2iTOlSj1/BCuo/X50bt25z5JTx5qoLVkXQrGG9\nPGe+EnuNsLFTSUo23tR65/5D+Hh5UjyXn021XuZnaz/Xr1yOG3/f5+gF4wWEhdsO0KxGJbNRAAaD\ngX5z19K30+vUqWR+PnTozyt0HT2PdJ2OB9pUFm47QLsXCn6fqdzkbGcXLVxgameze9ieHztmvOC1\neNEimjRt+shfa3lo6dIluLq68HOPHlbPbqzLd7LV5ch8tXFX4q4TNm5aVl0+EI1PmcKty9nVr12T\nG7duc/TkaQAWrFhDsxfyXq//DfUqBRKfkMjRi8aLHgt3HKJptWCzupyu09N/4W9cvGG86BdzO4G4\nO/cI9inNrXtJTIzczfRv3qGMR/5Hzj+pBo2bceJINNdirwKwbvlimr70Sr5eY+O61UweNQSdTode\nr2fD6uXUa2Tde+w9q1Q2Nk/939NA9SQ/6fu0y5wqtEqtVtfNsfwU0AGIBTYBB4GZ2csqirID+FGt\nVh9XFOUvtVpdOnP5KmAKxhEt7mq1upeiKFWBV9Vq9ThFUa4C1dRq9SO7wbUaTa47+9ChQ4waPRqN\nRkNAQACDw8PR6/V89fXXrFm9GjD+Mseg8HDu37tHyVKlCBs40HSj2y1btjB9xgz0Oh2Vq1RhUFgY\nLi4ufPnVV6jVarM5tjVr1iR80CCOHTtG+ODBpKam4uPjw9AhQ0w3K8tOlfH4OcGHDh9m5OixpvxD\nBg1En5HBl998y9qVKwDjL7YMGjyE+/fvU6pUScL696dcuSDOnvuT0L590el0XLt2naAg48lG5JrV\n3Lx5kyHDRph+oaVatefo1zuUYv/wAfhdMevcYC07tzKl+Wn3cgC8Kwdz++JVMnR6JrR6n3s3Cj6y\nYujkt/NV3s6vPK5N2qGyc0B//2+Sf1+OSqXCre1n3F9qOee8ePsvSYneiu76ZbCzx7VZB+x9yoLB\nQPrNGFL2RFrcy+VxnD3zd7+Lw1fiGbUxGm26joCSbgxq35gMg4GvF2xj1bchAGw7fZVZu0+Qrtej\nQkWXJtVo+3xF0vUZjN0Uzf6L17FRqShVzJk+bzQiOB+dLbavf/vYMgWpx7PnzmP23HmUKZPVyWJr\na8valSu4f/8+Q0eM5Nyff2LIMBAcXJ4BfftQqtQ/n+zb3s3fL2JEHz/N8Klz0GhTCfTzZmjPbmTo\nM/g8NJyIOcYpNe0++Q69PoO4GzfxLFUCJ0dHhoV2p0blimav9VyrDpz5fU2+3l9Xqnyuywurfbt/\n/z7Dhg/n3LlzGDIyCA4Opn///pQqVQq1Ws2IkSNJSEhAr9fTrFkzfvrxR4srqXZ/X87XNkLR7+dn\nLa/KkPH4QrllnjaPFG0qZX29GdrzGzIyMvgsdAiRs42//tb20x/Q6/XE3biVmdmB4b26Ua1SMGNm\nLeT3fYewtbEhuKw//bp9glc+vlwb7PL3JSf62EmGT5mJRpNKoJ8PQ0N/ICNDz+c9BhAx3/izye26\nfJ25j+PxLF0SJwdHhvX5gaijJ5i5aKVZPltbG9N6eQ+dv/0cffwUwyfPQqPVZmb+3lgveg4kYp7x\nJ9Tbffxt5j6+mZnZgWG9f6B65YpMnreYrbv2YQDcXF3p0+2z/N1cVpX/k+Ci3s8ZF4/kK++hP68w\nYtkmNKnpBJYpyZCuIegzDHw5fiFrw7/h+KU4Oo+YYzHKZeRnb6IEeDNk0Qb2nb6IjY2KD19qRKeX\nGj7inR5NVzckT+W2btnCjBnT0en0VKlSmYFhg3BxceH0qVNMmzaVadONP5l8+NAhRo0ehTazPR8U\nPpjSpUuze/cuJowfj1ar5a+//sLf358yZcrwy8xZhLRri0ajMZtm+1Lr1nzzjeXnskPClXxvY/Tx\nUwyfMierLvfqbqzLvQYRMdf4U8ntunbPqsulSuLk6MCw0O8y6/IStu7ej8FgwK1YZl2ukveOeYN9\nwab5RB87wYiJ0zPz+zK0z8/oMzL44qc+rFswE4CQjz435r8ej2fpUsb8fXtQvWruN/n9Jxknd+Z7\nnUPnYxi5ajua1HQCPEsw5MP/oTcY+HLKctb2+xQw3p/ll037TOdwXVs3oF2jGszecoDZWw5QxiPr\n3N3Wxsa0Xl7E1niyX73bs2Mbi+f+gl6vp0KlynTv1R9nFxfUZ0+zaPYMBo+bwt2Ev+nd7XMArsXG\n4OPnj62tLUMmTKeYW3Gmjx3B2dMnsFGpqFKtJp91/wnXPHTEVSzj9nTcjbWQbH+u3lPfgfDSmUNF\n/m/w/62j5XPgO+ASxhvaTsH4i0RzsnW0DAQ+BNoBu3PpaDkCzMd4vxdboLtarT5ckI6Wp1leOlqe\nJoXR0VLY8tvR8jTIb0dLUctLR8vTJr8dLUXtUR0tT7Mn6WgR+fMkHS1FLb8dLU+FZ20/P0FHS1HL\nb0fL0yCvHS1PiyfpaClqBe1o+bc9SUdLUXvSjpaiJB0tRe9p6Gj5T040U6vVV4G6uSyfiXEEy0Nr\nM/9bN1uZQcCgzIelsy3PfpS/mctrBz1xYCGEEEIIIYQQ4in3tPx88tPu2bukIIQQQgghhBBCCPGU\nko4WIYQQQgghhBBCCCv5T04dEkIIIYQQQgghhHWpZOpQnsiIFiGEEEIIIYQQQggrkY4WIYQQQggh\nhBBCCCuRqUNCCCGEEEIIIYR4LJWtjNXIC9lLQgghhBBCCCGEEFYiHS1CCCGEEEIIIYQQViJTh4QQ\nQgghhBBCCPFYNvKrQ3kiI1qEEEIIIYQQQgghrEQ6WoQQQgghhBBCCCGsRKYOCSGEEEIIIYQQ4rFU\nNjJ1KC9kRIsQQgghhBBCCCGElUhHixBCCCGEEEIIIYSVSEeLEEIIIYQQQgghhJXIPVqEEEIIIYQQ\nQgjxWDa2MlYjL6Sj5V+UwbN346Dz9/RFHSFfhk5+u6gj5FvfbiuLOkK+Tb2yrqgj5Eu6zbPX1GlL\nli/qCPkSez+9qCPkWwUn96KOkG8GJ7eijpAvhmfw2LNJTSrqCPmW5vhs1WW9oagT5J9r8StFHSHf\nEjS6oo6QL97PYHsRb1e6qCPki3edNkUdId8y0p/BBkMIZOqQEEIIIYQQQgghhNU8e13HQgghhBBC\nCCGE+NepbJ+9WRpFQUa0CCGEEEIIIYQQQliJdLQIIYQQQgghhBBCWIlMHRJCCCGEEEIIIcRjydSh\nvJERLUIIIYQQQgghhBBWIh0tQgghhBBCCCGEEFYiU4eEEEIIIYQQQgjxWDa2MlYjL2QvCSGEEEII\nIYQQQliJdLQIIYQQQgghhBBCWIlMHRJCCCGEEEIIIcRjya8O5Y2MaBFCCCGEEEIIIYSwEuloEUII\nIYQQQgghhLASmTr0lNi8eROzZ81Cp9MRXKECYWGDcHNzsygXHR3F+HHjSElJwcfHl0Hh4Xh5eQGw\neNEiVq9eRUZGBs/Xrk2fPn2xt7fn77//ZuiQwVy6dAmVSkVoaG8aNmoEwOHDh5k4YTzJyck4OTnx\nc4+e1KlTp0Dbsm/HVtYsmodepyOgXHm+6tEfl2LFLMod3vcHK+bPJD09Hbfixfn0h1ACywUDcP9u\nApOHDeR2/A0mLVpdoDyPY+cXjOuLr6NycESfeJcHv68g48H9XMvalvLB/Z3vSIycie76ZRwr18Wl\nSVsMKUmmMtqT+9Ce2l+omR/Hxs6O9iN60fqnzwj1b8i96zeLNM/B46cZPWshKZpUfMuUZuhPX+Ht\nWcqsjMFgYO6q9Uyct4x5owZQp1pl03MXY67Rd+x07iUm4eFWjKE/f02Fsv5WzRgVHc24zGPL18eH\n8GzH1kNqtZqhQ4dy9949Snh40K9fPypVqgTAps2bmZV5DFeoUIFBYWG4ubmh0+kYPWYMBw8eJCMj\ng/r169M7NBQ7O+s0v1s2b2bO7My2I7gCAzLfN6fo6GgmjB+HJiUFHx8fBg7K2r5ZM2eyadNGDBkZ\nKJUr07dff9zc3EhKSmLYkCGoz6sxZGTQ+uVX+Pqbb6yS+6E9v29l5cI56HU6AssF822vAbjm0l7o\ndDoW/jKZyBVLmLVyA6XLGLPr9XrmT5vA0aj9qFQ2KFWr8el3PXB2cbFKvqgjxxg7ZQYpGg0+Xl4M\n7tsT7zKeZmXUFy4xeMwE7t2/j4e7O/17fI9SwdiWzV6whIhNW1GpVJQvG0i/n7+jdKmSAJz58zw/\n9w+nfu1aDOr9s1XyAkRFH2LshEloNMZ/6/CB/fHOWZfPn2fI8JHcu3cPDw8P+vcJpVLFigCsWrOO\nxUuXotdn4OvrQ1j/vhbr/9gzlHv37jF35gwr5I1m3PgJWcfeoLBcjr3zDB02LOvY69vHdOylpKQw\neMhQtmzdytHDh0zrTJ8xg2XLV+Dh4WFa1r3bt7Rq2bLgmQ8fZeykacZ64e3F4P6heJcpY575wkUG\njxrHvXv38fBwp3/Pn1AqGuvFpStX6T94BHfv38fDvThD+vcmuFyQ2fo/9h7A3Xv3mTd9YoHzPmSN\n9iIhIYF+fXpz/cYNIiLXm623csUKFvw6H4CGjRrRs1co9vb2Bcq8dctm5mbL3H9gGMVyyXwoOppJ\nEx6eH/nQPywr88zp09i2bSsZGRkoSmV69+uHm1tx/vrrL0YMHcLVq1ewtbHltTfeoHOXjwuU96GD\np9WMWbiOFG0qvqVLMuTrTniXKmFW5uiflxm1YA0PNFqcHBzo1bkDdatWMCuzePNuhs1dxZkVk62S\n63F2bNvC4vlz0Ol0lCsfzM99B1CsmOX+1unSmTVtMquWLmZZxEY8y2Qds3cTEhgW1o+bN66zcFVE\noeY9ePQEY6bPJUWjwderDEN6fY93mdJmZQwGA/OWr2HirAXMHT+MOjWeMz23csNmFq6KJEOfga93\nGcJ7dLdY39p2btvCksx9HJS5j10fsY9nT5vM6qWLWZLLPh6RuY9/LYR9XJht3O+7/mDclBnoMzKo\nUqkig/uHUszV1erb8Mf2raxYkHl+UT6Y7qGPPr/4dcZkIpYvYe7qrPMLgLMnjzNtzHDSUlPx9Pbh\nx/7hlCrtafEaQuRGRrQ8BeLj4xk5ciSTp0xlXUQkvr6+TJ1i+YGq0aQQ2qsXAwaGERG5nqbNmjJ0\nyGAATp48yZIli/n11wWsXRdBUlISS5csAWDUyBH4BwQQEbme0WPG0rdvHx48eIBWq6XHzz/Ru09f\n1q6L4PMvvqRXzx4YDIYn3pa/bt1k3uSx9B4+ngkLVuLp7cuyOdMtyiXcuc20keF07xvO+PnLebHV\nK8waNwKA5MT7hP3wlanTpVDZ2eP2ygck71zFvUWjSL96FtcWHR5RWIVr8w5kZOtUAUi7fIZ7i0eb\n/oq6kwXg64hZpCanFHUMAFK0Wn4eNpHB33/BprkTaN6wDoMmzbYoN2jSbGKuxVPSo7jZcr0+g+/C\nx/LpO23ZMn8SnULasHrzDutm1Gjo1asXYQMHsj4ykqbNmjF4yBCLcr1CQ+nSpQvrIyPp2rUrvfv0\nAbKO4alTphAZEYGvry+Tp0wBYPHixVy9epVVK1eyZvVqLl68SESEdU6K4uPjGTVyJBMnT2HNugh8\nfH2ZNnWKRTmNRkOf0F70HzCQtRGRNGnajGFDjdu3fds2tm3bysJFi1m9dh0qVKYvSpMmTqC0Z2nW\nrF3HgkWL2LxpI3v37LFKdoA7t24ye+Jo+o+cyNRFqynj7cPi2dNyLTu8z084OVt2nvy+MZLL59VM\nmLuUSb8uJz09nTVLfrVKvhSNhp4DhhAW+hMbli2geeNGDB493qJcj4FD+LjTu2xYtoBPPniP0EHD\nAdgffZi1v21myawpRC6ZR9lAf8ZMMXZMHDp2ggHDR1O9amWL1ytw5j79COvfl/VrV9OsSROGDBth\nUa5n7358/NGHrF+7mq5dOhPadwAAp8+cZfovM5k5bSqRa1ZSsUIFJkwyr1N/7NnL2bPnrJa3V2hv\nwgb0Z33EOpo2bcrgoUMtyvXqHUqXLp1ZH7GOrh9/TO++/UzPfdSlCz4+3rm+fsd33yFi7RrTnzU6\nWVI0Gnr2H0RYn55sWLmY5o1fYPDIcRblevQbxMcfvMeGlYv55MNOhIYZP6/1ej3fh/an64fvs2n1\nUt5/+01WR2wwW/ePfQc4c05d4KzZWaO9uH//Pp99+gkVKlS0WO/YsWMsXrSQBQsXsTYikgcPHnDi\nxPECZb4ZH8+YkSOZMGkKq9YaM09/ROZ+vXvRt/9AVq8zZh6RmXnL5k1ERR1k4ZJlrFyzjoyMDObN\nmQPAxPFjCSxbllVrI5jz6wIi160lOupggTIDpGhT6TFhPuFfvM/GiQNoXqca4bOWm5VJS0+n2+iZ\n/PB+W9aP70e3d/9Hj4nzzcrcuXufVdv/vXOKWzfjmTJuFMPGTuTX5Wvw8vFh7ozc2+T+PX/COZc2\nOfH+fX78+jPKBVfIZS3rStFo6RE+ivAe3di4aCbNX6hP+LipFuXCx03latx1SpZwN1t+6s/zTJ23\nhDljh7Bh4QwqlQ9i3Mx5hZr59s14po4bxdCxE5n3mH084B/28U+FuI8Ls427diOeIaPHM338KDat\nXoq3Vxl277V+Hb9z6yYzJ4xm4OiJTF9iPL9YOCv3/Ty0d+77OeVBMqMG9ObbXv2YuXwdtes35I/t\nW6ye9VlkY6N66v+eBkXa0aIoSpCiKEmKouxSFGW3oii/K4rS6l98/zGKonR5gvXesmaOXbt2Ur9+\nfXx8fAAICWnPtm3bLMpFR0fj7+9PlSpVTOUOHDjAgwcP2LZtKy+/8gpuxYujUqlo1y6Ebdu2AnDw\n4EHatQsBoGLFilSpUpXoqCjS09MZMDCMqlWrAtCgQQP+/vtvkpKSLN47rw7t+4NqtetS2st40tuy\nzRsc/ON3i3K2dnZ07zcY/6DyAFSuVpNrVy8bn1Sp6BE+ijovNHniHHll718BfeLf6O9cB0B77hD2\nAZXA3tGirGO1huj/ukFG4t+Fnqugfhs8mQ1hll8Ii0LU8TP4+5ShakXjv3WHV1qw7+gJHqRozMqF\ntG5G+A9fWIz0OHZWja2tDa0bNwCgbasm9PriI6tmzHlstQ8JMR1bD124cIGkpCRaZn5Ra968OQkJ\nCVy+fJmdu3aZHcPtQ0JMx3Dt2rUJ7dULe3t77O3tqVatGpcuXbJK7t053jckJITtubQdh6Kj8cu2\nfe1CQjiYuX3lypdj0KBwXF1dsbGxoUbNmqZ8LVu1Ml3ddXMrTuXKVYiJuWqV7ADRe3dTo049PDPb\ni5f+1479uyzbC4C3P/qE97p+YbE89vIlKlevgb2DAzY2NlSrVZvYK9bZv9FHjuHv60NVxThyov3/\n2rA/+ggPHmR1Yp6/dJmk5GRaNW0MQIsmL5Bw9y6Xr8Zw4fIVnqtcCbfMK2gNaj/PxctXASjp4c6v\n0yYQFBhglaymzIcO4+/nR9Uqxg6c9u3eYP/BKLO6fP7CRWNdbtHcmLlZU2PmK1coUcKDkcOH4Olp\nvKJb+/laXLp82bSuRqNl3MTJfPX5Z9bJGx2Nv79ftmOvHQcOHMzl2EumZYsWADRv3sx07AH079uX\nN9980yp58pT58FH8fX2pWjmzXrzxGvujDpnXi4uXjPWimfFzrEXTF0lIuMflK1c5fuo0dra2vNSi\nKQBvtHmZnt9/a1pXo9UydvJ0vvq0i1VzW6O9UKlUjB03nqbNm1mstz4igg5vvUWJkiWxs7Nj2PAR\n1K1br2CZd++iXv36eGdmbhsSwu/bLTMfjo7Gz8+fypmZ32gXQtTBh21ceXr16YuTkxM2NjbUrlOX\n2JgYAC5duEi9+sbPlmLFilGlalUuXbxYoMwAUafP4+9Vmqrljcd3+5aN2HfiTx5otKYy6foMwj7v\nSINqxnpUu3J5bt+9T2K2ejR83mq+6PBKgfPk1f49u3m+bn28vI37u80bIezesT3Xsh98/CldPvvS\nYrlKpSJ85FheaNK0ULMCRB07gb+PN1UrGTsc2rdpzb7Dx3iQYn6hqd2rrQjv0R07W/Pzi5Lu7owZ\n0BPPzFGGtWs8x8UrsYWa+eE+LpNtH//xD/u48yP28aCRY2lUSPu4MNu4DZu38lKLZgQG+KNSqej1\nQzf+90prq29D1J7d1Mx2ftH6f+3YtzP384t3O3/C+59Ynl9E7fmD8pUqU/m56gC82akz7Tt+YPWs\n4r/raRjRolar1c3VanUz4HNgsqIoNYo61KMoiuIA/GjN14yJiSHAP+tkOyAggISEBBITEy3K+Wcr\n5+LigoeHB3FxscTm8hpXr14FjA1yRkaG2XpxcXG4ubnRIvPE1WAwsG7tWp6vXZvixc1HFORH/LVY\nvH2zpnR4+fpz/+5dkpPMt8W9RElq1W9kenw8+gAVqhiHchZzK45vYNknzpAfth6eZNzP1nGSnoZB\nm4Ktu/m0FpWLG841G5NyYJPFa9iV9qV4+y/x+KAnri3fRuXgVNixH+vKwaNFHcHk6rUbBPhkDcN0\ndXbCo7gbMTfMpzPVqlop1/XVl2Pw9fKkz5hptOn6PV/2H8G1m7etmtF4DGbV24fHVmxcnFkZfz8/\ns/X8/f25cvWqxfrZj+Hq1atTrlw5wDg89eDBg1SvXt0quWNjYvAPyHpf/39sO8y3z93Dg7i4OIKD\nK1Als7MVYP++fVSrZszXqNELlC5d2vQaZ86cMU07tIYbcbF4ZWsvvH39uX83waK9AKhcLfePhep1\n6nE0aj/JSYmkpaZy+MBeatatb5V8MXHX8PfzNT12cXHGw704sdevZ5WJvYa/r4/Zev6+PlyJiaPe\n8zU5fuoMN2/fQafT8/sfe2lUzzg1M7hcUKEMlY6JiSXAP6ueuri44OHuTmzctWyZY/H3z1GX/Xy5\ncuUqfr6+1K1d27R87779VK9WzfR4xqxZvP5aG3xzbHPB8lp+rpkfe7GWx56fH1cyP+Nq1qz5yNc/\nGBXNR5270DakPWPGjiMtLa3gmWNz1gsXY724ds28jK+v2Xr+fj5ciYlFfeESPt5e9A0fzutvd+Lr\nH3tx7Ua8qdz02fN5o83L+D1ilM6TskZ7Ubx4cYKCgnJ9/fPnz6NJSeGTrh/TIaQdUyZPQq/XFziz\nX7Ys/v65Z46NjcEvwDLztbg4KlVSqFRJASA5KYnft2+jSTNjR1Hd+vXZvm0rOp2OO3duc/b0GerU\nK1jnEEBM/G0CvLKmn7g6OeLh5krszTtmy1o3qGV6vOf4WYJ8ylDc1Xhlfc+xMyRrtLz6QtbxWNiu\nxcbi65e1H339/Ll3N4GkRMs2+bnqubfJbsWLE1A2qLAimomJu06AX9Zx4urijEdxN2Kvx5uVq/Vc\nlVzX9/Pxom7NrPZtb9RhalRVCidspmuxsfhk28c+/7CPqxbRPi7MNu78hUvY29nxWbcfef3tToSP\nHItGq8XarsfF4p1jP+f3/OLKpfMUd3dnWJ8efPnem4we2IfEe/esnlX8dz0NHS0marX6EjAU+EZR\nlG8URdmnKMoeRVF+AlAUJUxRlNmKomxVFOWUoiivZi7vkFl2t6IoYzOXdcksG6Eoyp+KonySufyD\nzHU3AeUzl9kqijJHUZSdiqLsVRSlZebyXYqi9M0caXNCUZRAYDxQXVGUaYqiBCqK8kfmensURXmi\n3gGtVouDo4PpsYODAyqVCo1G84/lABwdHdFoNBbPPVwO0KBhQxYvXoRer+f8+fMcOhRNalqqqey2\nbdto/VIrVq5cQd9sw7GfRFqqFnuHrBz2mduSqtU8cp1TRw/x26qldP76+wK995NQ2Tlg0OvMlhl0\n6ajszfeza+O2pBzajiHN/MNAf+8OaVfOkLhhHveWjUfl4IhL4zcKPfezRJuahqOD+f50cnBAo019\nxBrmkh484PCpc7z7emt+mz2OKsFBhI6yHDpeoIxaLQ6O5qOYsh9DYLzK/KgyOdfP7Rg2GAwMGzYM\nLy8vXn75Zevldvjn931YztHBPLtTju0DmDN7Fn8n/M17779vWqbX62nX9g3e7/gunbt0JtiKQ5VT\nU7U45NJeaDWPbi9yatC4GUHBlfi4/at0bteaB8nJtH69vVXyabSpODqa31/C+G+e1Q5oU1Mt67ej\nIylaDVWVSrRt8zKvvvU+TV5rz5HjJ/n0o/esku1RtFotjjk/J5zM/62N9SaXz5IcJ7vrf9vIvv0H\n+PoL4+iV8xcusv/AQTp/aL0reo/MYnbsaSw/+5ws629OVSpXoWWLFsyeNZOFv87n9JnTzJ03v8CZ\nNam57GOLemFZxsnRkRSNlqSkZI4cP8G7HdoRuXwhlStVpE+YcbrU+YuX2B91iM6dOhY4Z07Wbi9y\nSkpO4vix40yaPIU58+azZ88eIgs4TdJYn/OW2cHhn9vwfn1CafNKa/wDAvjf/14H4PMvvuTcmTO0\nbtmctq+1oeVLL5k6ZQpCk5qOo7356AknB3tSUnPv6FPHXGfkr2sY+Lnx312blsaoBevo98nbBc6S\nHzmPx4f7W/sP53BFSZNr++tAiib/X9wjt+5gT9QRvunSyVrxcpX6DOzjwmzjEpOSOXDoMCMG9WfF\ngtnEXbvOrPmLrL4NqY/4PpKf84sHSckcPxRFl6+7M2XhcuwcHJg1aazVsz6LVLY2T/3f0+DpSGHu\nMPAa8BblKJvzAAAgAElEQVTQGGgKvJnZyQHgp1arXwbeB4YrilIM6Ae0zBwVE6AoyouZZasDHYAQ\noJuiKCpgGNAKaAs8/MbwPhCvVqtbZJadkC1PolqtbgVsynyt0RhH4XydmXFb5nrfAXm+xLds2VLa\nh7SjfUg7zpw+TVq2D9/U1FQMBgMuzs5m6zg7O5uVA+OHoouzC045ntNqtab5hr169SIpKYkO7UOY\nM3sWL7zwgtnN71q3bs3233fQu08fPv/sU/7666+8bgYAm9eu5IfO7/BD53e4+OdZ0rNdMUxLM25L\nbvdWADi0dzfTR4bTa9g40zSif5NBl4Yqx1BSlZ0DhvSsbbAPrITKyYW088cs1tfdjEETvRXSU0GX\njubIThyCqlqU+//M2cmR1BxXkTWpqbg4523kTzFXFyqXD6Jm5YrY2NjQucPrHDt7nhQrXgExHlvm\nHT/GY8s5T2VyPvfwGHbOXF+n09Gvf39u3rrFuLFjsbW1feKsy5cto0P7EDq0D+H0mdOkpVm+r4uL\nZduRvXPVlD1bucmTJrFjxw6mTptuyg1ga2tLROR6Nvy2kc2bNrFq5conzg6wcc0Kvv3wLb798C0u\nnDtjNsIgzbTf8n4j2w2rlpF47y6LNuxg0YYd+AeVY+5k65wIOTs7kZqabrbMol44OVnUb602FRdn\nZ3bu2c8fB6LYtX4V+7dE8NrLrUz3byksxsy5fE5kuzmws7OzxciOh5kfWrZiFTNmzmb2jGmULl0a\ng8HA0BEjCe35M/b21ruPfu5Zcsn7mG3KTfPmzej80Yc4ODjg7u7OB5068YcV7jHk7JTbPk41O56c\nnZwfWaZYMVcqV6xAjWpVjW3ae+9w/NRpUjQaho4eT++fumNvpZtlF1Z7kZtixYrxyquv4urqSokS\nJXjjjbYcPHgg35lXLFvG2x1CeLtDCGfPnCY1l7Y1ZxYnZ2ezbcst85BhI9i+czfOzs4M6NcXgPCw\ngbRo1Yodu/ewefsODh+KZtvWgt+DwdnJgdR084s4mtQ0XJwspyUfU1/mq+EzCP/ifeo/Z7z3zfRV\nm3m9SV0CvQv/ppvrVi6ny7sd6PJuB9RnTxe4Tf435db+anK0ZXmxdN1vTJu/lLnjh+GZ44bF1rBu\n5XK6vtuBru924M9nYB8XZhvnVsyVlk2bUKpkCVycnXm3QwgHog9hDRtWr+CrTm/xVSfj+UV6Lvv5\nUd9HcuNSzJUaderh6x+AnZ0dbd/qyPFDUVbJKv5/eBp/dcgNSAYqAjuzLQvK/P/fAdRq9SlFUfyA\n54BAYIuiKADuwMORJQfUarVeUZRrmctLAUlqtfo2gKIo+zLLvQA0URSlceZj58wpQgAPz8quZa6f\n3VZgraIoHsAqtVqd5zOKjh3fo2NH45XNFcuXc+TIYdNzsbGxlPb0xC3HFJ6goHJs3ZJ1ApCUlERi\nYiKBZQMpF1SOuLjYbK8RQ/nyxo6LkiVLMXZs1k2sPv/sUypUqMjNmzc5d/YsLTLvOVG/fgO8vLw4\ndfKkaVlevNr+bV5tb7zqsiViFedOZHVI3LwWR4lSpXO9m/rJI9HMnzKOPqMm4V+2XJ7fz5r0d2/j\nUCFr2LnKwQmVkzP6e1nDex3KV8PO048SHxtvFqlycsatTWce7IlEd/0iBp0Og9Z4PwGVygYyCjZU\n+r+mXIAfm3ZnHRpJD1JITH5AWb+8DYv3LeNJcrb51raZvdS2NtbrJy4XFMSWXI+tsmZl4rINmzUY\nDMTFxVE+OJg7d+5w+MgR03OxsbF4enqapuENCg8nVatl4oQJBf4Fjnc7duTdjsarnitWLOdojvct\nXdoTN7ecbUcQW7fmsn2ZU/R+mTGdE8ePM3PWbFyzTWf5bcMGmjZriptbcUqULMnLr7zK/v37eOvt\nJ7/K+lqHd3itwzsAbFq7kjMnsqa5xT9sL3L5RZFHOX44igZNmuPoZOy4e6FZS+ZYqaOlXGAgW37f\nZXqclJxMYlIygQFZ01jKlQ0g7voN02ODwUDs9esEB5Vl8cq1NG5QDw934w0YX23VnNkLllgl2yMz\nBwWxeWvWfP+kpGQSE5MIzHYvmHJBZXOvy+WN7XBE5AaWrVjJvNm/UMbT+EXv5s1bnL9wkZ979QYg\nPV1HSkoKb777PquXP/k2lQsKYsvWrdnyPqybgdnKlMsl7zXTZ9yjxMbGUrJkSYpl3iNHr9dbpQOj\nXFBZtmzfaXpsrBdJBGabulIuKNCyXly7TnC5IGxUNiQlZ92DxiazTbt/PxH1hUv81CcMgHRdOikp\nGjp0+pg1i5/sBp2F0V48io+PD8nJyWbbZWuT/07ldzp25J3MzKtyZI77h8zbs2VOTkoiKTGRgMCy\nHIqOpmSpkgQHV8DR0ZGQ9h34/BPjvaeiDh7g2+++Q6VS4e7uToOGjTh25AitXy7YfVHK+3qxeX9W\n25aUoiHxgYayOTpO1DHX+XHcXMZ834U6VbJGC+78P/buO6zJq33g+DdsUBAnQ0BwxVX3ruKotvrr\nULv79m2rtrXaZVsHCA6GuFfdVVu1rjoRqNu6J7hnIw6GihMRhISR5PdHEIiggoYXbO/PdXG1kPMk\nd44n5zm5n3POc+Q0SSmpLNu0O+dv3l/6sST4x3zP8bx6vvcBPd/7AICwtas4dTw37qvxcVSsVKnA\nuzyVBtU93Ni8Mzd5mvIgleQHD6jm5vqEo4yFbtrO8tA/+X36OKpUenSYbxp56zj8kTq+Fh9HhVJW\nx8XVx5mbmeHi7JSvnzAz0XjujXfe5413DOOLjaGrOXMit56vX42nQsWi1XMVZxcSruYuYzVlrOLf\noTS2luaADbAhe++WjiqV6iWVSrUn+/FHY84AjuYp20SlUj0c9eW9nKDI/tHl+ZtZnucIyfMctVQq\nVcZjniOHSqU6AzTCkIwZq1Qqn2mHzo4dOxIZGZmzp8rSJb/TrVu3fOVatGhBQkICx7M76GVLl9Le\n2xtbWzu6vvoqmzdv5u7du2RlZbF8+XK6dTc8x7ixY1i6ZAkAR6KiuHXrFk2aNDFshjtyZM7Gb7Gx\nsTlfHJ9Vi7benDkWxfU4w0Zzf65eTtvO+ZdJpGs0zJ0QzKCgcSWWZAHIvHoRc/vyWLh4AmDTuD0Z\nMechK/cqduquddz7NYB7C4O4tzCIrBuxpGxaTIbqKNYN2lC287tgZgYKBTYNXyYj1jR34/inaNWo\nPtdv3ebomb8BWLxuAx1bNsXOpnAzWlo3acDtxHvsP3oSgFUbt9OknjLfdOHn8fCzdey4IUm4dOlS\nvL29ja6K1ahRg/Lly7Nx40YAwsPDcXFxwbNatXyf4d+XLMn5DG//6y8uX77M2LFjnzvJ8qhHX3fZ\n0iW8VkDf0bxFC24kJHA8+/0tX7aU9u29sbW15fy5c2z480+m/vyzUZLF8B7DWL5sGQCZmZkcPHiA\nWrUK3kvnWbRs14FTx6K4FmeIP3zVMtq/UrRlVVXdPTh2+ADaLENXffTQfpPdsaxls8Zcv3GTYydP\nA7Bk5Vo6tG1t3C68PCnv6MiGrYZN9sI2bsHF2QlPD3c8Pdw5fOR4zpKcPQcOUbO6p0lie5wWzZuR\ncCOBY8cNd3tZsnw53u3bGcdcvTrlHcuzYdNmAMIjNuS05Zu3bvHzzFnMmfFzTpIFwMXFmYN7drJz\n62Z2bt3M1Injadyo4XMlWQBatGhu/Nlbtgzv9u0f+exVN3z2Nm3KjjcCFxdnPKs9+Yv/7DlzmT5z\nJnq9nvT0dNasXUv79u2eeExhtGzaxNAuTpwCYMmK1XR4uU3+dlG+HBu2GDZuDduwOaddtGrRlDt3\n73LgsOEq7pr1ETRp2AAXZycO7djEro2h7NoYyrSxwTR+qcEzJ1keZYr+4kleffU1QkPXkZKSgkaj\nYdOGDbRs1eq5Yvbu2JGoqEhis2NevnQJrxYQc7Pmhj78RJ6Y22XHfPLEcaZNnpwzi2Dvnt3UzO7H\nqlXzZO8ew/BSo9FwJCqS6jWff3lkywa1uH47kaN/Gzbm/v3PnXRoWt9oRoter8dv1lJGfPG+UZIF\nIHyKP3vmj8n5Adgzf4zJkyyPatu+I8eORBKfven5mj+W0anr/24z3qJq2aQh12/c4uipswD8vno9\nHdq0LPSM2Zu37zBt/mJ+mRBYbEmWR7Vt35HjpbyOi6uPs7a25rUundi8fSc3bt1Cq9USGr6B1i2a\nm/w9tGrXgZNHo7iaPb4IW7mM9l2KNr5o3b4jZ04cI+aS4XvSlvBQk+0B96IzM1eU+p/SQPE8t/J9\nXkql0hPDTJDm2b/XwLBEpwuwHWgMqDEs5fEFfICmKpXqrewNc38FOgB/A81VKtUtpVIZCMwDugIN\nVCrV4OzlRWcw7MkSAzQEUoHjwCQMiZa3VCrVh0qlsgrwg0ql8lMqlbuAb1Uq1RmlUvktUAn4DQhX\nqVSNlUrlh8BllUoVmT0b5n2VSvX9495vmlrz2MreumULc+fOIStLS926dRgVEIidnR1nTp9m9uxZ\nzJ5juCXokagoJkycgEatxt3dncCg4JzNKpcvX8bKP/5Ar4fWrVsz1McHCwsLrly5gr+/HynJydg7\nOBAYGEStWobpqdu2bmX+/HlkZmaiUCjo3bsPb/XokRPXhcSiL884uGs7qxbNR6fNwqtWHfoP8cfG\n1o6L58+ycuEv+E+Yzv6/tjBnwmgqOxuvtho1dQ6X/j7H0l9mkJ6uISnxLk4uValQqTIjJue/Zd+j\n3EPz3xb0aSyqVqdM+x4oLKzQ3r/Lg79WolAosH/rS+6vyH9l3KFXf9Iit5J17TJYWFKmw9tYulQD\nvZ7MG7Gk7Q3Pt5fLk/h/93xLMR5lX6USg3YbbiPpXKcGty7GoMvSMu2V/5B0/aZJXmPWlfVFKh95\n8ixj5ywiTZNONVdnQgZ/jU6n40u/EMLnGer4rX6D0Gp1xCfcpHLF8thYWTF2yDc0rFOTo2f+JuDn\n+WRkZuLqVJnAgV/i4Vr4jSIznZ6+5j4qKooJEyeizv5sBQcFodVqGfD116xbuxYw3P0kMCiI+0lJ\nVKhYkYBRo3I2ut2yZQtz5s5Fm5VFnbp1CQwIwM7Ojv4DBqBSqYw2mW7UqBFBgYFPjCerkF3z1q1b\n+CXP644cZXjdM2dOM2f2bGbNNtxe/ciRKCZNmIhaY3h/AYFBVKpUiZDRwWzfto3yFSrkPKeLiwuz\nZs8h4fp1xo4J4eq1a2i1Who1asQwP/8Cv3DFJWfm+1th7N+xjRUL56HTaqleW8k3Q0dga2fHhfNn\nWfHrXEZNmkFS4l2GDzTcEeBaXCzOVd0wNzcncMpsbGxsmTdtPBfOn0WhMMPV3YMBg4ZRsXKVp752\nTYv8m+I9KurYCcb9PAu1WoOHW1VG+w9Fq9PS/0dfQpcabhF74dJlAsdNISk5mYoVyhPgO4jq1TzI\nzMpiws+z2X84CnMzMypWrMCIwQOp4eXJjHkL2bpzN0n376PVaqlYoQKveLfjhwFfPDEevc3Tr8ZF\nHTnK+EmTUas1uLu7MTpgJFqdjv7ffk/oqj8MMUdfJHD0GO7fv0/FChUIGOGPl5cnC35bxIKFi4yS\nLOYW5jnH5X2NOfPm89u8uU+O1+zpM0iijhxhQp62GRwYiFanY8DX37BujaF/jI6OJjA4mPtJ9w2f\nvZEj8PLy4vz58/j6+ZOVlcXVq1dzNmoNC13H3cREgoNHc+nSJczMzWn38ssM/P67fHvCPMos/el3\n3os6epxxU2eg1mS3ixG+aLU6+v8whNDliwDDfiuBYyeR9LCO/YZQ3dOQHDp24hRB4ycb+jRnJ0b5\nDjbaxPjha8xesIiFc35+ajwZ1uWeWgaev7/Ys3s306ZNRaPRcPfOHaq6uVGlShXm/jIPgDmzZxMR\nHoa1tTUdOnbku+8HFrhUUluEoee2rVuYP3cuWm0Wyjp1GZ4d89kzp5k7ezYzsmM+eiSKyRMnolGr\ncXN3Z2R2zOnp6UydPIkjUZHo9XqcnJzx8fOnWrVqXLp4kYnjx3L79m3Q62nd9mV+HDQ4393vAMpc\nLNqys8iz0YxdtAa1JgMP58qEfPNfdDod/UJmEzbZjxMXrvDJiKlUczFOnkz4vnfO3Yoeqv/+d5xd\nNaNIrw9ww71tkY/ZtX0rixf8glarpZayDoP9RmJrZ8ffZ8+wcP4cxk+bRWLiXX4aYNi7KT4uFtfs\nPnnijLlEq/5m3sxppKdrSLx7FxfXqlSqXCXn1vZP4pye8NQyj4o8foqxM+ehVqfjUdWFEN8f0em0\n9BsykrBFhtv59uj9tWF8cT2BypUqYGNlzRi/Hzl87CTzlq7GKU+SxdzcLOe4wrhuXfSNwXc/UseD\n8tTxovlzGDdtFvcS7zKogDqekF3H82dOQ5Ou4V52HVesXIWJhaljxYOnloHi7eNWrl3Pb0tWYGFh\nTtPGDRk2aOATl3tdyXy2ZVX7dmxj+W/z0Gq11Kit5Duf7PHFubMsWzCXwCkzuJd4F7/v8o8vRk+b\nTcXKVTiweweL5sxAoVBQzasG3wzxo1z5py8vU1ZxKB3f9IvJ8Z6vllwCoZCarN9a4v8GpSHRcho4\nClgD5oC/SqXaplQqvwb6AlpgvUqlGqtUKgMw7KviAHhhSIj8pVQq3wb8gHQMyZPvgM94JNGiUqk8\nlUplXwz7qcRgSOJsBpYCc4F62TEEqFSqTY9JtIQAJ4GzwNjs4x5kx/m9SqV67HSGJyVaSqtnSbSU\npGdJtJQ0Uyda/heKmmgpaYVJtJQ2hU20lBbPmmgpSYVJtJQ2hUm0lCaFSbSUNoVJtJQ2hU20lBZF\nSbSUFkVNtJQGz5JoKUnPkmgpac+SaClJhU20lCbPmmgpSZJoKXmlIdFSoiMglUoVg2H/lYIemw0U\nlFI+pFKpZj5Sdh2w7pFyi/I8/oDsPV5UKtVvGGalPCrfZUSVStUxz//nfc28u53KHDIhhBBCCCGE\nEP94ilKyNKe0K417tAghhBBCCCGEEEK8kF6oOb0qlSqgpGMQQgghhBBCCCGEeJwXKtEihBBCCCGE\nEEKIkqEwl0UxhSG1JIQQQgghhBBCCGEikmgRQgghhBBCCCGEMBFJtAghhBBCCCGEEEKYiOzRIoQQ\nQgghhBBCiKcyk9s7F4rMaBFCCCGEEEIIIYQwEUm0CCGEEEIIIYQQQpiILB0SQgghhBBCCCHEUynM\nZOlQYciMFiGEEEIIIYQQQggTkUSLEEIIIYQQQgghhInI0iEhhBBCCCGEEEI8lZm5zNUoDKklIYQQ\nQgghhBBCCBORRIsQQgghhBBCCCGEicjSISGEEEIIIYQQQjyVwlzuOlQYkmj5H1K8gG2yrqN5SYdQ\nJNrKFUo6hCKbdWV9SYdQZN949SzpEIpkWurZkg6hyCz2LCvpEIqkxsvvl3QIRabbv7GkQygyvSa1\npEP4x9PZOZR0CEVmVca+pEP4xzsaMKekQyiyRuEvVh+XdWh7SYdQZK5OHiUdQpGs7T6kpEMoMs2m\nLSUdQpEpq7x45xFherJ0SAghhBBCCCGEEMJEZEaLEEIIIYQQQgghnkohdx0qFKklIYQQQgghhBBC\nCBORRIsQQgghhBBCCCGEicjSISGEEEIIIYQQQjyVwkzmahSG1JIQQgghhBBCCCGEiUiiRQghhBBC\nCCGEEMJEJNEihBBCCCGEEEIIYSKyR4sQQgghhBBCCCGeykxu71woUktCCCGEEEIIIYQQJiKJFiGE\nEEIIIYQQQggTkaVDQgghhBBCCCGEeCqFLB0qFKklIYQQQgghhBBCCBORGS2lxOZNm5g/fz5ZWVnU\nrFmTgMBA7O3t85WLPHyYKVOmkJaWhourK0FBQTg5OQGwdOlS1q5Zg06no2nTpvj5+2NpaUlKSgqj\ng4NRqVTodDpee+01vvn2WwAuX75MSEgIiXfvYm5uzoABA3ilS5cixX44MorJ06ahTlPj4uJMUMAo\nnLNjekh14QKjx4wjKSkJR0dHRvgNo3btWgCkpaURFDKGLVu3cTzqcM4xvv7DOXf+fM7vDx48oFHD\nhkydNLFI8T1N5OUEpm6JIi0jC5dyZQjs1Q6ncmWMyhy5coOftx7hQXoGNpYWDO7ekmaezgD8uucU\nf564iAIFXpXLMeyN1lSytzNpjHkdOnGGifOXkKZOx7VKJUIGDcC5ckWjMnq9nt/WRPDzwj9YOGEk\nzRrUyXnsYuxV/CfPISk5BUf7soQM/pqa1dyKLd7CMrOwoNc4H7oO+hJft9YkXbvxP339w5GRTJk6\njbS0NFxdXAgKDMj5bD2kUl0gZMwY7iUlUd7RkeH+ftSuXRswtOPg0SFs2bqVY0eiCnyNQYOHkJSU\nxK8L5ps+/gtxTAnbTVp6Jq7l7Qn6uBtOjsZ9yJGL8UwN38MDdTo2VpYM7dWJZjUN//ar9p1gxd4T\naHU6qlYox6gPu+Jc3sH0cUYdYfK06ajVabg4uxA0anjB/cW4Cbn9xTAfatcy9Bcr16zlj1WrycrS\nUrWqKwH+fjg7Gx//01BfkpLu89u8OaaP/wWp54ciL15jyoaDpGVk4uJYlqD3OuHkWNY43svXmbbx\nEA80hv5tyJttaVbdFYBLNxMZtXoXSakaytlZE/R+J2o4VSi2eF/EmA+rYpmyfqehTVRwIOjj/8Op\n/CNtIjqOqWG7s9uEBUPfeYVmNd0BWLX3OCv2HDO0iYqOjProtWJtEwCHz19m8srNpKVn4FLRkeC+\nvXCuUM6ozPHoWCb+sZkHGg02VlYM/bA7zZWeACzYsJuw/SdQKKC6S2WGf/ImlcrlH7OUpph/3biH\n8P0nSEvPoGvz+gz5oBsKhaLY4nVo3ASPfv0xs7Ul4+ZNLk8aT8adO0ZlzGxtqT5oKGXr1kWnSSd+\n4a/c27cHANcP/0Olrq8BetRxscRMn0bmvXvPHdfhyMiccaSri4vROPIhlUpFSEhI7rlu+PCcc92m\nzZuNxquBAQHY29szbNiw/GO2Ro2YMnky0dHRjB03jsTERMzMzPh6wAC6FHGs+dj3Ex3PlLA9pGVk\n4lregaCPuhbQJ19lasTenP5iaK8ONKthPO5ZsfcE49bt4uTUH0wS1+McOhvNpGXhpGkycK1UntFf\nfYhzRUejMsdUV5iwNIxUtaEd+3zSg+Z1axB57iIDJszHpWL5nLKvtGjAjx++UawxV2nXisYBQ7Ao\nY0fq1etEfu+POuGmURmXLt685PcD5tZWZNxL4viI8SQePw1A/aHf4tGjGwozM+6dPs+RwQFkJqcU\na8wAZw/sZP/6Zei0WVR28+T1rwZjY1c2X7m/I/ewL3QZWRkZ2Nk70O3zH6ji7sWp3VvY9vssyjjm\njrGbv9qD5q/1LPbYxT+DzGgpBRISEhg/fjwzZ80iLDwcV1dXZs6Yka+cOi0NHx8fRgUEEB4RQQdv\nb0YHBwNw6tQpli9bxuLff2d9WBgpKSksX74cgGlTp1KpcmXWh4WxdNkyNm7cyN69ewEYMngwb775\nJqHr1zN23DiGDx9OSkrhO780tZqhw/wIGDGCiPXrDDGFjM1XbqivH30++5SI9evo2+czfIcPz3ns\nk959cXF2yXfMuJDRhK9bm/NTR6mkx5tvFjq2wlBnZOK7ejcje7xM2MC38a7jTkjEQaMymswshqzc\nybA3WxP6/dv069gYn1W70Ov1HLp4nbBj0fze7w3Wfd8Lj4oOTN1yxKQx5pWm0TB4zM8E//AVm36b\nRsfWzQicviBfucDpC4i9mkAFR+PBularY2DQZL54/y22LJrOxz27s3bzjmKLtyi+DptP+oO0Ennt\nNLUaH99hBIwcQUTYery9vQkOCclXzmeYL717f0ZE2Hr69unDMP/cdvxp7964uDg/9jX27N3LuXPn\niif+9Ex8Fv9JwIevEjG8L94NahC8crtRGU1GJoN+C8f/vS6E+felf7c2DFkUgV6v58SVayzecYTF\nAz8k3L8v1Z0qMHn9btPHqVYz1G84ASP8iFi3hg7e7Rg9dny+ckP9RtDn00+IWLeGvp99iu/wUQCc\nOHmKxUuWsXjBPCLWraa6pyeTpv1sdOyeffs4d/5vk8cOL049P6TOyMRn+TZGvduB8CEf0aGeJ6ND\n9xjHm5nF4CVb8evZnvWDP+SrLs0Yumwber0erU7HoCVb6dOxMX/6/If/vPwSoZHFU7cvasxp6Rn4\nLIog4D/diBj5Jd4NahK8cotxvBmZDPo1DP/3uxI24gv6d3+ZIb+FG9rE5Wss3hHF4h8/JnzEl4Y2\nEbqz2OJ9GPPQuasI6NOTP8f+QMdGSoJ/Dzcqk5GZxfczlvPDu10JDxnIt71eweeXVQAcOHuR0L3H\nWD78K8JDBlLNqRKTVm4u1THvPXWBdXuO8rvfl2wc9wPnYq7z58GTxRavmY0NNf1GcHnKJE71+ZR7\nhw7iOfCnfOWq9f+azMS7nPj4Qy6MGo5zj55gZoZD02ZU7tads999zanPe6O5ehWPfgOeO640tRof\nHx8CRo0iIjwc7w4dCB49Ol85H19fevfuTUR4OH379mWYnx+QO16dNXMm4WFhuLq6MmPmTADGjh1L\n2Pr1OT91lEreeustAAYNHsx/P/6Y9aGhhISEMHzECO7fv//87yc9E5/fNxLwQVci/HrjXd+L4NXG\nYxpNRhaDFv2J/7udCRv2Gf1fa82QxRvR6/U5ZW7fT2XtwTPPHc9T49WkM2TGEoK+/ICNU4bRsWk9\ngn5bY1QmIzOL7yb/xo8fvk7EJF++e68bQ2YuyXn8pRoe/DnZN+enuJMs5na2tPllElE/jmBjm//j\n+pZdNJ84yqiMpYM9redO5PC3vmx6+Q3OTp7Ly79NA8Cj1//h3KENW195h41tX0dhbkbdH/oVa8wA\n9+/cZOvimXwwNIT+kxdRrrIzu1cuLLDcpl9/5r2fgug/eSF1WnVgwy+Tch6v3aId/ScvzPmRJIuB\nwtys1P+UBqUjimKmVCrfKekYnmTXzp20bNkSFxdDsqFnr15s27YtX7nIyEjc3NyoW7duTrmDBw+S\nmkzmZiIAACAASURBVJrKtq1bee2113BwcEChUNCjZ0+2bd0KwCtdutCnTx8AHBwcqFu3LjExMWi1\nWvr168cbbxg66Vq1amFpacm1a9cKHXtkZBRuVatSr65hxkSvHm9x4NAhUlNTc8pciL5IyoMUOnfq\nCECnDh1ITLzH5ctXABjp78e77/R64uvs3b+fjIxMOnbwLnRshYr/8g3cypelrqshW92zSS0OXrpO\nanpmTplMrY6RPV6mnmslAFpVd+HuAw0pmgyib96jXtVK2NtYAdCyugsXbz3/FafHOXziLG4uVahX\nqzoAb7/Wif3HTpKapjYq17NrB4J+/AoLC+NJa8fPqTA3N6Nru1YAvPVKe3y++rTY4i2KDcEz+DNg\naom8tuGzVTXns9WrZw8OHjRux9HR0aSkPKBzp04AdOzYgcTERC5fvgzACH9/3nmn4K5GrVYzdeo0\n+vf/qnjij47DraIjdd0NVyV7tW7AQVUMqZqMnDKZWh0BH71GvewyrWp7cDcljRR1OhXK2hHySXcc\n7GwAaFnbg5hiaMeRUUdwq+pKvTrZ/cVbb3Lg0GHj/uLiRVJSUujcsQMAnTp4k3gvkctXrlChQnnG\nBAXg4GBIILZq2YKY2NicY9UaDVN+nsGAfl+YPHZ4ceo5J96L13Cr4EDdqpUB6Nm8Dgejr5Kanife\nLC2j3u1APTdDmVY13bj7QE2KOoOTsTcxN1PwSgNDf/N609oMfrNtscX7IsYceSEOt0rlqOtuSLL2\navMSB/+OIVWTnhuvVkfAf7pRz8NQppWyGndTUg1twt6OkE9ez20TymrE3EwstngBIs9fxq1yeepV\nM8wA6tW+KQfOXiJVnRtzllbLqM/eomVdQz02reXBraQUktPURF+9SX3Pqthnx9yqbnUuXrtVqmM+\neO4SrzStR7kytlhaWPBh55ZsO3q22OJ1aNyE9BsJpF2MBuD25o2Ua9YcM1vbnDIKS0sqdnqFa8uX\nAqC5Gs/5IT+BToedV3UeXFChTTP0jcknjmPr5fXccT06juzVs2fOOPIhw7kuhc6dOwPQsWPHnHPd\nzl27jMarvXr2LHC8um/fPjIyM+nYoQOZmZkMGDCATtnnzrp16mBtbc31hITnfz/R8bhVLEdd9yqG\neFrV56Aq9pE+WUvAB13z9MnuOX3yQxNCd/Fl15bPHc/THD57EbcqFajnZZhN06tjK/afUpGq1hjH\n+8V7tKpvmMXZVFmdW/eSSU5VF/icxc2pXStSY69y77RhttKVFetw6vgyFmVyZ26XreaGVq3h/rkL\nANzadxi7qi5YOtiTfOESR4cGodWkg17Prf1RONR4/rb8NBeOHsCzfhPKVTL8uzfq2J3zh/Nf2DA3\nt6Dnt36Uq2wo59mgCYkJ8cUen/h3+McnWpRKpSfwUUnH8SSxsbG4ubvn/O7u7k5iYiLJyclPLGdn\nZ4ejoyPxcXEFPkdMTAwAbdu2pVIlQ5IgNiaGs2fP0qZNG8zNzXmtW7ecL+OnT50CoFq1aoWPPS4O\nd/fc6ZeGmMoRFx+fp0wsblWrGh3nVrUqV7Lja9So4VNfZ87cX+hfDF+eYu/ex61C7qwPO2tLHG2t\niU/MrXt7Gys61fUADEty1h+Lpkk1JxxsrWnu5czJuFvcvJ9KllbHjvNxtK7havI4H4q5eh13l9wp\nvmVsbXB0sCf2uvEym8b1ahd4vOpyLK5OlfGbNJvufX+g/4hxXL1RvAPkwrpy6FiJvXZsbBzubvk/\nW0btODbuKe240WOff+68ebzx+uu4uhZP24i9dQ/3SrlT6O2srXAsY0vcndwv8fa21nR6qSZgaMeh\nB8/QtEZVHOxs8KhcnsZehvemychk49HzdHyphunjjIvD3e2R/qJcOeLir+aWeWw9x+Lh7k7j7P5C\no9GwYdNmOuVJvs6dt4A3/q87ri75Z8iZJP4XpJ5z4r1zH7eKj/RvdjbE38nTv9la06m+V268Uedp\n6uWCg501FxLu4lLenhGrdvDWxBV8u3Aj1xKT873Ovznm2FuJuFfKnfaf0yZuJxnH27BWbrwHT9G0\nhltum6iep01EnaNjw5rFFi9A7I27uFXJXUplZ2ONY1lb4m7dNfpbl2b1c37fdzoaT6eKONjZ0qKO\nFycuxXEj8T5ZWi1/HTtHm/rF145NEbMC0Op0RmXjbxVfQsvGzQ3N9es5v+s0GrKSk7Fxze3bbKpW\nRZeeTuVXu9FwwULqz5iNQ5OmACSfPIF9vfpYVaoEZmaUf7kd948+/2zZ2NjY/H1wvnNdAWM2Nzeu\nxMTkO/5x49XZc+bwVT/DrAVLS0u6d8tdprVjxw4c7O2pUb3687+f2/dwr/hIn2xnQ9ydRz5/2f2s\nXq8n9NBZmlavmpPc3Hf+Cg/SM3itScHjJlOKvXEb9yqVcn4vY2ONo70dcTfvGP2ta8vccfHek+fx\ndKmMQxlDki7hzj2+HPsLrw8ayw/TFnEzMfe9Fgf7Gp48iM1tH1mpaWTcS6KsV+53heToy+i1Wqpk\nX8Rze/NVEo+fJjM5haSzKpLOqgCwtC+L+1uvcW1L8c+kTky4Snmn3DFXeScX0pKTUD8wnrVftnxF\nvF5qBoBOq+X0nq3UapabnL8Ze4mlwT8x96fP2DBvEpq0B8Ueu/jn+Dfs0TILaKlUKkcBLwHlMbzv\n71Qq1SmlUnkR+AV4E7AGugDvAA1UKtVgpVJZFjijUqk8lUplNLARuAUsBH4FrAAt8IVKpYp7lgA1\nGg0VKuQOIKysrFAoFKjV6pwrtw/LWVtZGR1rbW2NWq3O99jDvz+k1Wrp2aMHt2/f5ocff6RmTeOB\n3I0bNxg2bBg+vr7Y5rniUpjY88dkgzpPdl6j1mBlZV1g3IURGXUEvR6aN2tW6LgKS5OpxcrC3Dg2\nS3PUGVn5ym47G8P4DYewt7Fi0oeGKz11XSvyZuOavD51DbaWFlRxKMNvn3c3eZw58aZn5KtvGysr\n1Hmunj5JSmoqR06f59dxwxn9U39m/L4K3wkzWTolqDjCfWFoNBqsHvPZekitUWNl/UgZm6e34+jo\naA4eOMiypUs4cbJ4pqprMjOxemT2krWlBeo8M7Me2nbiAmPX/IW9rTVTPu9h9NjUsN2s3n+KJtWr\n0ueVFqaPs6D+wsYatUZtVCZfPT/ybzHl5xmsXreOJo0a0efTTwDDTJgDhw6x/PdFnDjx767nnHgz\nsrDOF6856owC4j11iXFh+7C3tWbyJ68CkKJO59jlBH758g0C3+3E7G1R+K/cwaIBxTd1+kWLWZOZ\nVXCbKCje4yrGrt5uaBNfGMczdf0uVu8/QZPqbvTp0qpYYn1InZFRQB1bFtiOAVTxN5iwYhPjv3oP\ngHrVXHmrbRO6DZ2CrbUlTuUdWOxbPLPITBVzm/o1GbVoPZ+82pZyZWxZu/sI6Zn5z/OmYmZtgy4j\nw+hvuox0zGxscn43L1MW87Jl0WVkcOqLPpRr3oJaIwM58el/SLsYze1tW2m89A+0GjUZt+9w7qfv\nnzsuQ//65PGY+gllCjNejYyKAr2e5s2bGz3HyZMnGTJ0KDqdjgnjx+c75z7T+8nMwsqykJ+/E9GM\nXbfT8PnrY5jJrcnIY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Pw8n4zMTFydKhM48Es8XAs3I+ahb7xMu9+BfZVKDNptmC7vXKcG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L99FBo0qG/uIytX0rxZsyI+Ul7xke3KE0yit2whIMCfsEIBu+KY//U3zJk3D5PJhF6vZ936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\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pZWOvVlO6b3/cKlbErNMTN+8X7vz9FwCBb7yFT4tWgAVtbAzRP0wl486dJ9Zly7r3x59/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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xu1DiQrfocI2"
      },
      "source": [
        "Our goal is to avoid multicollinearity by dropping features that are closely correlated with each other. For example here it is TotalCharges and MonthlyCharges. So we will drop TotalCharges."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRIcVa-8ouOZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1088
        },
        "outputId": "cb3ee438-d766-4427-ab9c-9815bda34c40"
      },
      "source": [
        "\tdata.pop('TotalCharges')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         29.85\n",
              "1       1889.50\n",
              "2        108.15\n",
              "3       1840.75\n",
              "4        151.65\n",
              "5        820.50\n",
              "6       1949.40\n",
              "7        301.90\n",
              "8       3046.05\n",
              "9       3487.95\n",
              "10       587.45\n",
              "11       326.80\n",
              "12      5681.10\n",
              "13      5036.30\n",
              "14      2686.05\n",
              "15      7895.15\n",
              "16      1022.95\n",
              "17      7382.25\n",
              "18       528.35\n",
              "19      1862.90\n",
              "20        39.65\n",
              "21       202.25\n",
              "22        20.15\n",
              "23      3505.10\n",
              "24      2970.30\n",
              "25      1530.60\n",
              "26      4749.15\n",
              "27        30.20\n",
              "28      6369.45\n",
              "29      1093.10\n",
              "         ...   \n",
              "7013    3756.40\n",
              "7014    3645.75\n",
              "7015    2874.45\n",
              "7016      49.95\n",
              "7017    1020.75\n",
              "7018      70.65\n",
              "7019     826.00\n",
              "7020     239.00\n",
              "7021     727.80\n",
              "7022    7544.30\n",
              "7023    6479.40\n",
              "7024    3626.35\n",
              "7025    1679.40\n",
              "7026     403.35\n",
              "7027     931.55\n",
              "7028    4326.25\n",
              "7029     263.05\n",
              "7030      39.25\n",
              "7031    3316.10\n",
              "7032      75.75\n",
              "7033    2625.25\n",
              "7034    6886.25\n",
              "7035    1495.10\n",
              "7036     743.30\n",
              "7037    1419.40\n",
              "7038    1990.50\n",
              "7039    7362.90\n",
              "7040     346.45\n",
              "7041     306.60\n",
              "7042    6844.50\n",
              "Name: TotalCharges, Length: 7043, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV1tSYT6o7DS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "0b60bee4-969c-47f9-c4de-4da1a297c1d8"
      },
      "source": [
        "# Run info again to make sure TotalCharges has been dropped (popped off).\n",
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 20 columns):\n",
            "customerID          7043 non-null object\n",
            "gender              7043 non-null int64\n",
            "SeniorCitizen       7043 non-null int64\n",
            "Partner             7043 non-null int64\n",
            "Dependents          7043 non-null int64\n",
            "tenure              7043 non-null int64\n",
            "PhoneService        7043 non-null int64\n",
            "MultipleLines       7043 non-null int64\n",
            "InternetService     7043 non-null int64\n",
            "OnlineSecurity      7043 non-null int64\n",
            "OnlineBackup        7043 non-null int64\n",
            "DeviceProtection    7043 non-null int64\n",
            "TechSupport         7043 non-null int64\n",
            "StreamingTV         7043 non-null int64\n",
            "StreamingMovies     7043 non-null int64\n",
            "Contract            7043 non-null int64\n",
            "PaperlessBilling    7043 non-null int64\n",
            "PaymentMethod       7043 non-null int64\n",
            "MonthlyCharges      7043 non-null float64\n",
            "Churn               7043 non-null int64\n",
            "dtypes: float64(1), int64(18), object(1)\n",
            "memory usage: 1.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YiHnJp34Cvza"
      },
      "source": [
        "Rerun corr chart after cleanup. TotalCharges should not appear in the corr chart. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ox5889JMCuiD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 931
        },
        "outputId": "7e8af303-09cb-4cbd-983e-0ebfaf318732"
      },
      "source": [
        "corr = data.corr()\n",
        "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values, annot = True, annot_kws={'size':12})\n",
        "heat_map=plt.gcf()\n",
        "heat_map.set_size_inches(20,15)\n",
        "plt.xticks(fontsize=10)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hIpJoEUIIIYQQQgghhDARSbQIIYQQQgghhBBCmIjco0UIIYQQQgghhBCPJY93\nzh8Z0SKEEEIIIYQQQghhIpJoEUIIIYQQQgghhDARmTokhBBCCCGEEEKIx5LHO+ePjGgRQgghhBBC\nCCGEMBFJtAghhBBCCCGEEEKYiEwdEkIIIYQQQgghxGPJ1KH8kURLMXE4PJyZM2eSlJSEk6Mjfn5+\n2NvbG5VRVZXAwEBu37lDpYoVGTt2LHXr1gVg+44dLF26lJSUFGrXro2vjw/lypUjJSWFadOnExYW\nRlpaGi1atGCUtzelS5fm448/JubaNWxsbAzrWLJ4cY71mlOp0qXpPdmLzsM+xdulFXeuxT21dWd3\nODyCGbNno0nS4OjogJ/PBByyfwfnzxMwcTJ37tyhYsWKjBs9irp16wCQlJSEX+BEdu7azfGIw4Zl\nkpKSCJw8hT9PncKylCUvtWnN0K+HYGlpadb6HNi7i02rlpOakoKrR00GjRiHXdmyOcodOfA7635Y\nwsOHDylXvjyfDPXGzaMWAHdvxzNv4gT+jr3O3FUbzRKnuba795ixnD13zvDvxMREGj/3HLOmTzNL\nPXR1CWfmrNmZ+7GvTy778XkCJ07M3I/HjDbsx0lJSfgHBLJz1y6OHYkwW5y5CTt+iumLfiBJo8XJ\nvhoBI7/EoVpVozLp6eksXxfCnO+DWDbDj6aN6uf4nKDgn5k47zvO7Nn0tELn8JmLTF+9laTkZByr\nViLg0344VKloVObY+StMC9pCoiYZW6syjHzvNZrVqwnApWs3GLvkJ24n3Kdi2WcI/PxNajmbrx/c\ntXMHy77T9de1atVm3AQfypYrl6NcRHg4c2frjguOjo6M88k8LixZ+C27d+8iLS0NRanHqLFjKVeu\nPDdv3mRyYACRkVewLGXJKz168GH/j0wS9+Ejx5gx91uSNBocHezxH+eNQ/XqRmXUCxfxnzqTO3fu\nUrFiBcaNHIZSR9efXLoSyTj/ydy+e5eKFcoTMG4UtTzc8Rrvz1n1vOEzEhMTeb5RQ2ZN9jdJ3LnX\n5Sgz5i4gSd/v+I8dhYN9trqcv4j/1BmZ/Y7XMJQ6tXV1uXyFcQGTuH3nLhUrVNDVpaaH2eLNbvfO\nHfzw/XekpqRQs1YtRk/woWzZnG3oSEQ482fPQqNJwsHBkTETfKmub0MnTxxn6sRAkpOTcXB0ZIJ/\nANWqVc/xGaZ0+OgJpn+7lCSNFkeH6gR4/x8O1asZlUlPT2f52g3MXfIDy+ZMoclzDQ3vJSVp8J0+\nlx2//sbJX382a6xZhR07yfSFy0jSaHCyr06A1zc4VM+lf/xpE3OWrmDZrIk0fe5Zw3vrt+5g5YZQ\n0lLTcHKojt+IITmWN7ffftmMjRN8AAAgAElEQVTJTz9+T0pKCjVq1uKbURN4JpdzgpSUFH5YOI/N\nPwXx46ZtVK2esy/8bv5sDuzbw/INW8wac9jx00xbulJ3XKpelcDhg3GoVsWoTHp6OsvWb2HO8jUs\nnzaBpg3rGd67eDWGMdO/5c69BCqWL0vg8C+oXcPFrDHv2bWTlct1+6ZHzVqMHDch133z2JFwFs6d\njSYpCXtHR7zG+lDd3p6UlBSWLJjLoQN/8CBZS+++b/LW+x8C8PeNOGZMCiQ29hrp6fB6v7fo1bef\nyesQ9udfTPtxA0maZJyqVyHwyw9xqFrJOP5zF5myfD33k7TYWFvhPeANmj2rO5dZv2s/K7buIS0t\nDefqVfAb/EGO5c0l7Pgppi1eYTiXCRzxRe5tZl0oc5atZvl0H8O5TPiJ0wwcMxHHLPtmpzYt+b9P\n3n0qsYt/H5k6VAwkaTR4eXnhM2ECW0JDade+Pf4BATnKeXl7079/f7aEhjJgwABGjR4NQGxsLFOm\nTGHB/PmEhoTg5OTEvPnzAQgKCiIyMpIN69ezaeNGLl68SEhIiOEzA/z9CQkONvw9zSQLwOCQpSQn\nJj3VdeYmSaNh5KjR+Iwbx5bgTbRv146AwEk5yo30Hs1HH37AluBNDPjoQ7zHjjW8937/ATg6OOZY\n5rtly3n48CEhGzewbk0QZ86eIzjUvCcnN2/EsXzeDEZNmsXsFeup5uDE2u8X5igX/8/ffDvFjyFj\n/Jj1w0+06dSVpTMnA5B47y4+QwcZki7mYM7tPjkwgNBNGw1/9RSFnj16mLUuXt6j8Bk/ji0hwbRr\n1w7/wMAc5bxGedO//4dsCQlmwEcfMWpMZl0+6N8fR0cHs8WYlySNlhEBM/AbNpifVyzA88Xm+M1a\nnKOc3+zFRMZcp3LFCrl+zj+34tmwdbe5wzWSpH3AiAWr8P2kL9ume+H5QgP8lhsneR48TGHIzB8Y\n+uYrbJk6gi/7dmXkgiAAUtPS+Gb2jwx4tQM7Zo7i3S5t2Pjr4dxWZRJxsbFMnzKF2XPns2FzCI5O\nTixcMD9HOY1Gw9hRXowZN4GNwaG0bdeeyYG648LOHds5fDiMlavXsn5TMGlpaSz//nsA5syagVuN\nGmzYHML3P64gNHgz4YfDnjjuJI2GkeN88Rk9kq3rg/B8qTX+U2bmKDdirC8fvfc2W9cH8fH77+Lt\no0uWpKam8o33OAa8/w7bN67hnTdeZ2PIVgCm+I1jy08rDX/16tah5/+6PXHMj6zLWB98RnuxdcMa\nPF9qg/+U6TnrMs6Hj95/m60b1vDxB+/iPSFrXcYy4P132b7pJ97p9zobQ7eaLd7s4uJimTVtCjPm\nzmPtpmAcnJxYvGBBjnIajYbxo70ZNW48P20KoU27dkydpOuT7icmMs57JKPGjWdDyBZatnqR3Tt3\nmDXuJI2WEb6T8B35DdtWf49n65b4zZiXo5zfjHlcjb5G5UoVc7z37uChODqYNxmUXZJGywi/qfiN\n+IqfVy3Bs3UL/Gbm3N5+MxcQGX2NypWM+8dTf51nwfLVfD8jgK0rF1G3pjszlyx/WuED8HdcHItm\nT8Nn2lyWrNmEvYMTK5bkrAOAv/f/YWNnl+dnXb5wnrD9+8wUaaYkjZbhE2fjP/Rzti+fg2erpvjO\nXZqjnO/cpVy9dp3KFcsbvZ6amsbXvtP55M2e7PxxHu/27M7G7XvMGvONuFjmzpjClFlzWbl+Mw5O\nTny3MPd902/sKEaMHseqDcG0fqkdM6fo9s1tIZs5e/o0361cw/dBP/Hz1hD+PH4MgGmBfjRr2YoV\nP21ixrxvWbpwPlcuXzJpHZK0yQyf8R3+gz9g+7f+eDZ7Dt9FQUZlHjx8yJeTvuX/3u/D1vm+fPXO\nawyfqTsGnboQyfy1W1jmM5Rt8/2o4+bMjJVP58JLkkbL8MBZ+A8bxPYf5+H5YlN8Z+c8l/Gds4Sr\neZzLNFJqs235XMOfJFnEk3hqiRZFUb5QFCVMUZTfFEUJVxTl5QIu760oyosFKN9NUZRDiqIcVBTl\nqKIog7O8Pkj//331/31eURTfgsRjSuHh4bi4uFC/vi6j2rtXLw4dOsT9+/cNZS5cuEBCQgIdO3YE\nwNPTk/j4eC5fvsyv+/bRokULHB0dDcvv3q37sdOkSRO8vbwoU6YMZcqUoWHDhly6ZNpO+Uls85/H\nVp9ZRR0G4eERuDg706C+7kpI756vcTAszOg7OH/hIgmJCXTs4AlAh/btiY+/zeXLVwAYP2Y0fV/v\nneOzL1y8SPOmTSlVqhRWVlY837gxFy+a9zuIOPA7DZs0o6q97kd7x+49CPs95wmGZenSDBnrj4u7\n7sp+vYaNiYm8rHvTwoIRflNp2rqt2eI053bPav+BAzx48BDP9u3MUxEy9mPnLPtxTw4dCstlP06k\nY4cOAHh6tjfsxwDjxozh9ddfN1uMeTl8/BQujvY0qKtLqvXu3pEDR09yP0ljVK5nlw74DRtM6dK5\nj8aatGAZn7/X1+zxZhV+9iIu1arQwEN3lbJP++YcPHWe+xqtoUxKaioTPu5Liwa60QhN6nrw9+17\n3Luv4cT5SCwtS9G5eSMAerzUlJHvvWa2eH/7bR/NW7TAQd9fv9arF3t+yZmcOhIejrOzC/X07alH\nz14cDtMdFzxq1sRr9BhsbGwoVaoUTZo2I+rqVQAuXbhI8xYtAShbtiz1GzTg0sWLTxx3+JFjuDg5\n0aCe7opl7x6vcPBwBPfvZybKz1+8REJiIp3a6/qMDu3aEB9/h8tXIjlx6jSlLS15uYNuH+zRvQsj\nv/kyx3r2Hwzj4cOHeLZt88QxP74uyqPrkpBAJ32f0aHdS7p+50okJ/7MqEt7fV26MvKbr8wWb3b7\n9+2jafMWOOgTzD169mLvnpxt6GiErg0p9XRt6NXXehGub0O//7aPuvXq07DRcwC83/8j3nnvA7PG\nHX7sBC5OjjRQdKMR+7zSlYMRx7ifZHyxpWe3l/Ed+U2u/cyE4UN4o0d3s8aZ3eHjJ3FxdKBBXV3/\n0bt7Zw4cOZ5L3J3wGzGE0pbGg8UrV6jA9PEjqValMgBNnnuWi1eink7wemF/7OP5ps2p7qA7J+jy\nak/++DX3pMNb/T/hvY8/z/W9tLQ0FsyYzPufDjJbrBkOnzitOy7V0Z2f9OmW+3GpV2dP/IYOpHRp\n4+1+/KyKpaUlnV/S9YevvdwOr4EfmjXmA7//RpNmLbDX75uv9OjFb3t+yVHu+JFwHJ2cqavfN7v3\n6MmRw2Ek3b/PkfAwXu7aDWtra8qWLUf3V1/jN/131aP36/yvp+58p7q9A84ursREXTVpHQ6f+gsX\nh6o0qOUGQJ9OrTlw8qzRMfVhSiq+g96jZSNdH9q0fm3+jr/DvftJVK5QlhnDPqFaZV0So2mDOlyM\num7SGPOM/cRpXByyt5k/c7aZLp74DRuU57mMeDxLC4ti/1ccPJVEi6Io7sCnQFtVVdsD7wLjCvIZ\nqqpOVlX1UAHWNwt4Q1XV1kB74ENFUTqrqrpDVdWMS/ve+s8+oarqhILEY0pXr17F1SVzKKOdnR0V\nK1YkKjraqIyLs7PRci4uLlyJjMyxvKurK/Hx8dy7d49GjRrh4aEbzpySkkJYWBiNGjUylF25ahX9\n3nyTN/r1Y9OmpzfUP8OVsGNPfZ25uRoVhatr9u+ggvF3EJXLd+DszJXISAAaN34u189u2aIFe37d\nh1arJSEhkbDDh3mxVUvTVyKL2JgoHJwy62Pv5MLd27dJTLhnVK5Cpco83yIzf3ki/BC16+uGO5ct\nVx4ntxpmjdOc2z2rhYsWM/CzT0wTdB6uXo3C1cXV8O/c9+Oox9SlsVljzMvVmOu4OmWOpHnG1paK\n5csSdS3WqNzzzyp5fsb+w8dITEqim6f5fiDnJjLuH1ztM4cF29lYU7GsHVE3bhm9lpFIAdj/51+4\nO1Sj/DO2qFGxOFapxJjFa/nf8CkMmvY9MX/Hmy3eqKtXcc7SX7u4ZPbXRuWiruKcbd+oULEiMdHR\n1K2rULeu7rtITEhgzy+7adte96O/WYsW/LJ7FykpKfzzz9+cPX2Gps2bP3HcV6NicHF2MoqnYoXy\nRMXEGJdxcjJazsXZkStXo1AvXMLRwZ4xfpN49Y13Gfx/XsRcN25fAN9+t5zPB5j3x9DVqGhcXDL3\nw9zrEm1UXwAXZyddXS5e1NclkFf7vs3goSOIuf50fkgAREddxTlLX+Ps4srtvNpQtnOLChV0beji\nhfNUrFgR7+H/x5t9ejJulBd37tw2a9yR0ddwdcocfWhnZ0vF8uWIijHeds83bJDnZzzqPXO5Gn0N\nV+cs/WNG3Dn6x5xTKQGcHe1p1jhz+tMfh4/wXIO8+1JzuBZtfE7g6OzCndvxJGRrMwD1G+Z9TN0e\nsgn3mrWo92yjPMuYSuS1WFwdM0dZP2NrQ8Xy5bh63XiK+fMN6ua6vHr5Kk72VRk9bQHdP/qagWMn\nERP7t1ljjs62zzm5uHA7l+0cHRVltA/b2dlRvkJFrsVEY4EFaWlphvdsbe24FqM7j2jXoRN2+tFG\nZ06dJP7WTRo1fsGkdYi8/jeuDpnT+Z6xtaFiuWe4mmXbPWNrQ+cXmxj+vf/Yadyd7Cn/jB3O1asa\nphBlvPdc3aczrTIy5jquTlnbjO5c5mr2ffUR+1/s3zf51MufV/oP4Rvf6dy4eSvPskI8ztMa0VIB\nsAGsAFRVvaCqantFURooirJXUZQ9iqIEK4pSUVEUd/2ol2X6kSjfASiK8oOiKK8qilJGUZTl+jJh\niqJ00b9/QVGUOYqijAEGAvNUVY3Rry8R6KKq6m5FUforijJdUZQRQGNFUTYpiuKpKMoGRVE8FEXZ\np/87qiiKqv/sPoqiHNCvc4b+tf6KonynKEqIoih/KYrycWE3jlarxcra2ug1a2trNJrMDKzmEWWy\nL29lZYWFhYXR8unp6UycOBF7e3u6dOkCQNu2bXnttdf4ae1aJk2cyNx58zhy5Ehhq1GiabVarK2s\njF6ztrZBkyWDr9VosbJ69PeUm7f6vUFKSgrtO3XG8+XOuLq60PYl8/4YfZCspUyW+pTRt4lkbd6x\nnjoWwbYNa/hw8DdmjS0rc273DOERR0hPh2ZNmz55wI+g1WqxylGX7PuxBivrbGVs8l8Xc9EkJ2Nd\nxjguG2trkrTJ+Vpem5zM1EU/MHbIZ+YI7zHrfoBVGeMrmTZWZdAkP8i1vBp1namrtjB+gG7kUEKS\nhqPqFd7s1JotU0dQ392ZUYvWmC9erRbrx/TXGeUe1+7Hjvame9fOuLi68r//vQrAZ58P5NyZM3Tu\n6Mlrr3Sn48svG5IyT0KTrMU6e9u1tjbeV3MpY2NtTZJGl2Q+euIkb/bpSah+etBoH+OpdeFHj5Ge\nnk7zJs8/cbyPosm138lWl1zK6OqiyVKXXoSuC6KeUpfRE3JO9zUXXRvKjC2jDWmztaHk3PokG2u0\nWg2JCQmEhx3iy6+/IWjdBqysrJg9I+f0KZPGnazFyqqM0Ws21tZotNo8ligeNMnJubQFK5I0BY87\ndNde9h8+yhf9n+50hORs54n5OSfILv7WTULWrab/wKczekurTcY6e3uxskKTz+NSQuJ9jpw6x5uv\ndmHb97OoX8sD76k5p6qZUnK2fjuv/j3XfVPfvzdt2YptocEkJCRw9+4ddm3fxoMHmcezG3GxvNXr\nVbyGDmHIsJFUrGTae59okx9gXSa37Z7HMTUyhsnL1uMzKGebDt0Xxv5jZ/jyLfNN285Km9u+WoA2\nU61KJV5u25Ipo4YQ8t1MqletjPdk87YZ8e/2VBItqqqeBMKBK/qEST9FUUoD84DPVVXtBOwCvtAv\n0hQYDTQHXlEUJetE3bcBrX5kTB8gY3J7GWC7qqqBQD3gRLYY7mb79zTgrqqqfbK8dkVVVU9VVT2B\nS8BoRVHKAmOBjvp1uiqKkvEruZE+hl5AoY88tra2PEg27gS0Wi12trb5KpP9veTkZNLT07HVL5+S\nksLYceOIu3GDmTNmGG7C2r9/f17u1AkLCwtq165Nt27d2L9/f2GrUaLZ2tqQ/MD4IKLVarGzy/Yd\nPMjlO3jEXGaAWXPm4uzsxB/7fuWPfb+i0WhY/uMK0wWvt2PzeoZ+2I+hH/bj4l9neZilPg8e6NqE\njW3usUb88RsLp/jhNXGmYRrR02DO7Z7h5x076N6ty5MH+xi6OHOri51xmeRHlykKtjY2JD80jkuj\nTcbO1iaPJYwtXLmeV19uh5vT07+/jK21FQ8ephi9pnnwADsbqxxlj5+PZPC0Zfh+0pcWDXTTpMra\n2VDPzYnnartRqlQpPuzejhMXrpKUx0llYaxbu5Y3+vTijT69OHvmNMm59NdZ2zyATZ7tPrNcwMTJ\n/PLrb9ja2jJ+7BgA/Hwm0KFTJ/b+tp8dv+zlSEQ4u3ftfOI62NrYkJyj7SYb76s2tnmWKVv2GerV\nqc1zDRvotvPb/Thx6jRJWX6A/LzzF7p37vTEsT5O7v1Oco5+J6++SVeXOjzX8Fl9Xd7MURdT2/DT\nWt56vTdvvd6bs2fOGG1nwzE/Wz9iY5N7n2Rra8czZcvStHlLXFzdKF26DP3efofwsHwNGi40Wxsb\nHjx4aPSaJjnZ6FynOLK1ydledP1jweJeE7yNb39Yw7JZE6lWxfw3Bt2y8Sc+f+d1Pn/ndc6fO2N0\nnvgg+dHnBLlZOncmb3/0KeXKl398YRPQbffc2kv+jktln7GjXi13Gtevo9tPX3+V42fPFypB9iib\n1q/l/X59eL9fH86dPWPUb+e5b+ZxvmBrZ8f/XutFsxYtGTzgA8Z7j6BZi1ZGN9O1d3BkbfBWlq5Y\nzdKFCwg78IdJ62NrbUXyw+zb/QF2ttY5yh7/6xIDA+bh98X7tGhonNBfs30fC37aynK/oVSrlPt9\n3Uwt1301+UG+24yHqzMjP/+QyhUrUKZ0aQa//wbhJ8+YvM38G1haFP+/4uCp3aNFVdUP0E3hOQGM\nBHYDLYCliqLsA94HMsZ7XVRVNU5V1TTgOroRMRmaAfv0n3kdSFYUpbL+vXD9f9OBQk+8049OuaOq\n6kbgWcAN2KmPsw6QMZ/ikKqqqUBMthgLxMPd3Wh6QUJCAvfu3cOtRg2jMtFZhjWnp6cTHR1NzVq1\nciwfFRVFtWrVKK8/GPr6+ZGs1TJn9mzDE4ZSU1NRVdUojtSUlBxzXP8rcn4HibrvwM0ts4yHO9Ex\n1wz/Tk9PJzommpqPedLEwbAwunXpTJkypbG1tcGzfXuOHjP9lKluvd9g1o/rmPXjOjq/1oe4a5nt\nJS4mmkpVqvJMLne+//NoOD/Mn8noqXOppeQ+9NlczLndM+zf/wdt27xkqpDzlOd+nLUu7h657Mcx\n1Kz59JJbuanp5kxUlid+JSTe515iIjWcc95kODe/HowgaNM22vUdQLu+AwBo13dAjuG65uDhVJ2o\nGzcN/05I0nDvvgY3e+MnmahR1xk2byVTv3iHds9ntnOnKpVIyPIDuVQp3dHZspTpjtL93nqL9ZuC\nWb8pmNf7vkFMlnYSHRVF1arVKFfO+MeLu7u7UbnEhAQS7t3D1a0GEeHhXLqku++KtbU1vXr3IezQ\nQQAOhx2ia/fuWFhYUKFCBVq2epHjR48+cR083GsY7YcJiYncS0jALcv0Jg93N6KvZU4DSU9PJyrm\nGrU83HFycCAhMfN+RaUsdacflqUyT0N+PxhG29atnjjWx/GoUcNoP8y1LjXciI55RF3uJxrey60u\nptb3zbdYu3EzazdupnffNwxTCQBioqOoWrUq5bI9uapG9jaUmNGG3HBwdOR+YkJmHUqVwrKUee9X\n4OHmSlSW9pGQeJ97CYm4uTg/YqmiV9PNxWiakKF/dHF6xFLGNm//hdWbt7Ji7mSjaZrm1OP1N1m8\neiOLV2/klV59ic1yTnA9JprKVarm+rSzvEQc/IPv5s/m3de68s2nH3Dz7xu8+1pXo4s6puTh5kRU\nlmlCCfeTuJd4nxr53H5O9tVIzHLfJcuM/dTStPtpnzfeYuW6Taxct4meffoa7ZvXoqOoksu+6VbD\n3ahcYmICiQn39InP0gwaMpSV6zczZ+FSLC0tqVm7Ng8ePGBbaDCpqakAODo582Kbl4g4bNoEqYeL\nA1FZpgkl3NdwLzGJGo7ZnsoWGcPQaUuY9n+f0L6p8VSyzXsPEvTzPlYGDjeahmRuHq7Oxm2mgOcy\nN2/fMZoqlJqahoWFBaXN/JRQ8e/1tO7RYqEoio2qqudUVZ0NtARcgLJAB/0okhdVVR2iXyQl20dk\nPeNNz/ZvKyBjMmNGb/8XuiRO1hhqKIry2GfpKYpSFxgEZMyfeAAczRjpoqrqC6qqrs4lzkKflTdv\n3pzY2FiOHT8OwKpVq2jXrp3R1ZJatWpRqVIlfv5Z9zjD0NBQHB0dca9RA09PT8LDw4nU3+dhxcqV\ndOume2LDL3v2cPnyZSZNmkSZbEMBvxoyhF27dgEQFxfHnr17advWfDc+Lc6aN2tGbGwcx47rBkKt\nDAqiXduXjL+DmjWpVKki27brnswQumUrjg667+BR3GvU4Lf9uisOqampHDh4kNq1zPckH4Dmrdtx\n+lgE1/U3Sdu6fjWtO+Yc1ZGs1bJoqj/D/CbjUuPpPZo0gzm3O8Ct+Hjib9+mRg23x5Z9Us2bNzPe\nj4OCaNe2bbb9uKZuP96+HYDQLVtwdHTIV13MqcXzDbl+4x+OntI9DnvFxi20b9Us31eBQpfN4feN\ny/l9wzJ+37AMgN83LMv3yc2TaNGgNtdv3uaYqrs58ort+2n/Qn2jES3p6emMWfwTY/v3pmk946RW\ny2frcPNOAgdO6RLP6/ce5oU67jmGrJtKO09PIiLCuarvr1evWkmXbjmfsNO0me64cELfnlYHreKl\ntu2wtbXl5InjzJ4xw3BFdP/vv1G7jm5OfI0a7uz//XdAd4X0SEQ4NWvXfuK4WzR5getxNzh24k8A\nVq5ZT/s2Lxq3bw93KlWqwLaduhuzhmzbgaODPe5urrRs3oSbt25x8LDuseUbgrfwwnMNDdOobsXf\nJv72HdzdXDG3Fk2bcD02a13W0b5N62z9joeu3zHUZbu+Lm60bN6UmzdvcTBMd21nw+ZQXniukdGU\nMHNq196TI+GZbWhN0Cpe7pp7G4qLi+XkCV0bWhsURJuX2mJra0s7zw4cP3aMSxcv6Oq3eRPNWpj3\n3mEtmjTm+o2/OfbnaQBWrNtE+xdb5LufKSotXniO63F/c/TPMwCsWB9coLhv/HOT2Ut/ZPFUX6pX\nrfL4BcygVdv2nDwaTkxUJACbfwqi/ctdC/QZG3b/TlDoToJCdzJ76QqqVrcnKHSn0TRlU2rZuCHX\n//6Ho6f/AuDHjVvxbNkk39u91fMN+Sf+NgeOnARg3bZfeOFZJcfUElNq086ToxERRF2N1K1z9So6\ndcm5b77QtBlxsbH8qd83168J4sU2un1z946f8R3jTVpaGjf/+Ycd27bwctfuWFlZEfTDMnb9rHvC\nWVJSEieOHqFW7TomrUPLhgrX/4nn6FldMv/HLb/g2awRdjaZ/Vt6ejqj5v7AuM/eplkD4/XfuHWb\nWas2s2T8EKpXzvnkMHNq+fyzXL9x03Auo2szTfPdZvYeiGCIzzTu6y++rNy8jVYvNMwx5VGI/LJI\nT083+0oURfkEaAd8qKpqun4ESgRwGZipqup2RVHeAv5BN2Vng6qqzfTLHgH6Aj7ABqAiupvqfq4o\niiuwS1XV+oqiRAINVVVNVBTFDd2ol66qql5QFKUc8DPgiy7B01BV1eGKosSrqlpZURRP4EvgHf1y\ng1VVPaFfvx26xE0zVVX/1j+daAnQOcvnlAVOq6rq/qjtoNVo8tzYERERTJ02DY1Gg6urK/5+fqSm\npjJo8GA2bdwI6J5Y4uvnx907d6hcpQo+EyYYbnS7c+dOFi5aRGpKCvXq18fXxwc7OzsGDhqEqqqG\n0S2gu+Gmn68vp0+fZvKUKdy7d4/SpUvz3nvv0ad37k9v+cbO9CMdylWvyrDffgLAoV4t/r4YSVpK\nKrM7vcOd6zdMtp45iafyVS7iyBGmTJth+A4CfCeQmpbGwC++ZPP6dYDuCTi+/gHcvXuXKlUq4zNu\nHB4e7pw99xfeY8aQkpJCTMw13N11P5xDN20kLi6OgImTidQ/FaRhw2cZO8qbsmXLPjamc3dSC1Vn\ngEP7fmHdD0tJS03Bo049Bo4Yg42tHRfPneGn5YsZM3UuB/bsZOHUAKplezzyhFkLufTXWVYtnkdy\nspY78bewd3SmctVqjJuR+yMhs6tfMX9XAMy13QHOnvuLL7/+mr0FnDqRblG4HHTEkSNMnToNjVa/\nH/v6kpqWxqDBX7Bpw3pAvx/7+3P3zl3dfjx+HB4eHpw7dw7v0Rl1icHd3R3Q/QAqiNLxkYWKPfzE\naSYt+B6NNhk3ZwcCR35FWmoan3n7EfL9HAB6fvw1qalpRF+Po1qVSthYWzPRewjP1TM+0Xq2Ux/O\n7Cn4zbXTYwv3NK7ws5eYvCoEjfYBbvZVCPz8TVLT0vl86lKCJw/nxIVIPvD7lhoOxrn2KYPfoYGH\nC8fUK/gu28iDhyk4Va3EhI9fx83+sXl5I0kNOue77O5dO1m6aBGpqSko9eozdoKuvz5z+hSLvv2W\ned/q7td+9EgEM6ZNQ6vR4OLqynhfP6pWrUpycjKzZkznSEQ46enp2Ns74DV6DDVq1ODSxYtMmzKJ\nf/75B9LTadW6DUOHDX/kaEXbB3fzfC+riKPHmTxrHhqtFjcXZwLGeZOamsbAb0awefUPgO5pPb6T\npnPn7l2qVK6Mz+gR1NTvl8dO/InflBk8ePgQJwd7JngPx1U/muHsX+f5YpgXv27bnO/tCEBh99Wj\nx5k8c05mXcaP1tXl62FsXrMisy4Tp3Ln7j2qVK6Ezxgv47pMnpZZl1EjDXXJrwTLxx8D8rJn9y6+\nW5zZhkaNm4CdnR1nTzmkD94AACAASURBVJ9myaJvmT3/W12cR44we8ZUNBotLq6ujJ3gS5Wqura9\nb+8eFsydg4UF1KxVG68xY6lUqfKjVmtQPqlwx+jw4yeZPHeRbrs7OxE4ahipaWl8PnwMwT/qHsPa\n68PPSU1NJfpaLNWqVsHG2oqJo0dgaVkKL/8pPExJJeZ6LB5uuhFIW1Z9V6AYLNKyX8vLT9x/Mmn+\nEjSaZNycHQn0HkpaWiqfjRhPyA+6bd2z/2B9/xhLtaqVsbGyZuLooRw+dpIlq9ZjnyXJYmlZyrBc\nQUSWLvxomP17drNq2WLSUlOpVbceX3uPw9bODvXsaVZ9twj/mfO5HX8L7y9199qKibqKo7MLlpaW\nBM5ZSNVqmSMabsRex/urz1m+YUu+1++RdLnAMYefPMOkhT+QpNVSw8mBwOFfkJaWxqejAgldOgOA\n1z4dpmsvsTd0xyUrKyaN/JLn6tXm6Om/8JmzRLefVq+G7zefFWqK698V8p+s/vWXXSxfuojUlFTq\n1qvHiDG6ffPcmdMsW/wt0+bqvvfjR48wf6bufMHZxRXv8b5UqVIVjUbDRJ+xXFB1T036ZNAXdHhZ\nd6Hs4vnzzJ0xhdu3b5OamkKbtu0ZNGQopR4xmq769YgC1zf8tMqk79eRpE2mhmM1Ar/qr9vuvnMJ\nnTuBE39d4r0x03KMcpk29BP+OHGGJRu2Y18lM8liWcqS0LmFeOZIuYInJsNPnGbSt8t1sTs5EDhS\n32a8Awj9TveU09c+GaprM9f1bcbaikleX9Gwbi2mL13JngMRWJYqRa0aLoz96mOjfTe/LF0bFZPJ\nK+axsmp98ycQntD7N88V+XfwtBItlsAUdMmWRHT3U5mMLtGyBN2IFA26REd5Hp1o2QEsAmqhG80y\nSlXV37MmWvTLvQjM1n92GjBbVdX1iqL0JzNBsgcoh24q05fARnT3fMn6y/xVoAu6e8YkA8fR3Y/l\nQ0yYaCnuzJFoeVrym2gpjp4k0VLU8ptoKY4Km2gpDgqbaCkOCptoKQ4KkmgpbvKbaCmWSvC++iSJ\nlqJW2ERLcVCYREtx8SSJlqJWmERLcVGQREtxU5hES7FRiERLcfFvT7Ssrtag2P+mfeefs0X+HTyV\nRIvQkURL0ZBES9GQREvRkERL0ZBESxEpwfuqJFqKhiRaioYkWoqGJFqKhiRail5xSLSU3DMUIYQQ\nQgghhBBCiGLmv/mIGSGEEEIIIYQQQhRIcXl8cnEnI1qEEEIIIYQQQgghTEQSLUIIIYQQQgghhBAm\nIlOHhBBCCCGEEEII8ViWFjJ3KD9kRIsQQgghhBBCCCGEiUiiRQghhBBCCCGEEMJEZOqQEEIIIYQQ\nQggh/p+9+w6L6lgfOP5dehHEglIVRT1qNPaW2KIxMfcmtjRT7tWYelNMMSp2xN57iTXW2BU09kSN\nURF7bDl2imI0ogKyC+yyvz8WFpZFRVwE83s/z+OT7Nk5u+8Mc+acnTMz56HkqUP5Ix0tQgghhBBC\nCCGE+H9BUZRJQFPACHytquqhHO99AbwPGIDDqqp+U5DvkKlDQgghhBBCCCGE+MdTFKUVUFVV1WbA\nh8DUHO95Ar2BFqqqNgdqKorStCDfIx0tQgghhBBCCCGEeCh7jabY/3uItsAGAFVVzwKlMjtYANIy\n/5VQFMUBcAMSClJO0tEihBBCCCGEEEKI/w98gJs5Xt/M3IaqqjpgKHAJiAYOqqp6riBfIh0tQggh\nhBBCCCGE+P/IPAQmc2RLf6AaUAlooihKnYJ8qCyGK4QQQgghhBBCiIeye/jUnOLuGpkjWDL5AfGZ\n/18DuKSq6t8AiqLsBRoAJx71S2REixBCCCGEEEIIIf4/2A68AaAoSn3gmqqqSZnvXQFqKIrimvm6\nIXC+IF8iI1qEEEIIIYQQQgjxj6eq6n5FUY4oirIfyAC+UBSlO3BXVdX1iqKMA3YpiqIH9ququrcg\n36MxGo22i1o8kE6rfWoLW5OhL+oQCuzrErWLOoQCGzHtzaIOocBcvUsXdQgFZv/ql0UdQoHZ344p\n6hAKTF+mclGHUGAOty4VdQj/L2mMGUUdQoEZHVyKOoSCe4rLHc3TO5g748KRog6hwPQNOxV1CAXm\nlHC5qEMoMKOj68MTFVNXKFPUIRRY1XIeT/3cmgfZ5Fe72P+mffXaySL/G8iIFiGEEEIIIYQQQjyU\nxr7I+zCeCk9vt74QQgghhBBCCCFEMSMdLUIIIYQQQgghhBA2IlOHhBBCCCGEEEII8VB2MnUoX2RE\nixBCCCGEEEIIIYSNSEeLEEIIIYQQQgghhI3I1CEhhBBCCCGEEEI8lMZexmrkh5SSEEIIIYQQQggh\nhI1IR4sQQgghhBBCCCGEjcjUISGEEEIIIYQQQjyURp46lC8yokUIIYQQQgghhBDCRqSjRQghhBBC\nCCGEEMJGZOqQEEIIIYQQQgghHspOpg7li4xoEUIIIYQQQgghhLARGdFSTByMimLixImkpKTg5+tL\nWFgY5cuXt0ijqiojRozg9p07lPLyYuDAgVSrVg2ALVu3MnfuXPR6PVWqVGFoaCgeHh7o9XrGjR9P\nZGQkGRkZNG7cmH4hITg4OBATE8Ow4cO5Hh+Pi6srYWFh1Khe/THzcYgJkyejTdHi6+tDWOgQfHLn\n49w5ho8czZ07d/Dy8mJQ/35Uq1YVgJSUFMJGjGTb9h0cO3TQvE9KSgojRo/hj5Mnsbezp/nzz/Ht\n1z2xt7d/rHgfh52DA51H96Vdr48JCWjKnavXiyyW3Bz8g3F//lU0Ts4YEm9z75dVZNy7m2da+zK+\nlHzraxIj5qC/egnn6g1xa9EBY0qSOY3uj33oTu5/IrFHXYpn0rZDpKTp8S3pztDOzSlf0t0izeHL\n15my/TDJqWm4ODrw/SuNaRDkA8D83/5g0/ELaNBQybsk/V5tSlkPt0KJ9XHr+5p161m2/CcMGQb8\nfP0IHTwQn/LlSU/XM2b8eKIOHcKYYaRxo4aE9OmDo2PhNdmRx04yfvaPpGh1+JX3ZnifL/HxLmuR\nxmg0snBVOFPmL2PBhDAa1K5h9TnLNmxm5LR5nP5lnU3jK6w2Mj09nbFjxxJ16JC5jQzp2xdHR0eu\nX7/OsGHDuHrtGkajkXffeYe3337bpvkq7uX+IE977ON+WGyOfUTvL/DxLmORxmg0smBVBFMWLGfh\n+FBz7FHHT/HZgJH4lsvOa9vnm/DdR+89mdiPnmD8rAWkaLX4lS/H8L7f4FMuj3JfuY4pcxezYNJI\nGjz7jPm91Zu2smRNBBmGDPx8yhHWu6fV/oUa++yF2XWm79d515mV65kybwkLJo2gQe2a5vdWhG9m\n+fqfMWRk4O9TjqHff4lvOe8nE/dTWuYAB89eYsLq7aSkpuFbuiTDPuiET+mSFmmOnY9h3KqtJGtT\ncXFypE/X9jSsFgTA/C17idh/gpTUNNo1qEnvt15Goym8O9pbt25hXmZ7HVylCqGhQ/Hw8LBKFxV1\nkEmZ5wVfXz+G5jgvJCTcon+//ly7dpWIjZvM+yQnJzNq5AjOnDlDRkYGL7dvz+eff2HzPEQe/cPU\nPuqy2sev7lPXNzBl/lIWTByWq65vYfmGzRgMBvx9yzO01+dPpK7n5eCR44yfMZcUrRZfn3IM79cL\nn1yxGI1GFv60hqlzFrJg6ljqP1urSGLNsmfnNlYuno9Br6di5WC+DhmCe4kSVun0ej0/zp7GhpXL\n+HHtz5QtZ6o/OzdvZM7U8ZQuk/03+3eXt3jtddteA4h/NhnRUgykaLX07duX0CFD2BgRQctWrRg2\nfLhVur4hIXTv3p2NERH06NGDfv37AxAfH8+YMWOYMX06EeHh+Pn5MW36dACWLVvGlStXWLN6NevW\nruXChQuEh4cD0K9/f9q0acPGjRv55ptv6NO7N0aj8bHy0adff0IHDWLjhnW0atmS4SNGWaXrE9Kf\nD7r9l40b1tHjg26EDBxofu8/3Xvg6+Nrtc+8BQtJT08nfO0aVv20jNNnzrIhYmOBY7WFz8Pnkpqc\nUqQx5MnBEY+X3yd51xruLB1L+pUzuL/Q5T6JNbi37kJGjk4VgLRLp7mzbJz535PqZNGmpROyeg+D\nOz5P+NddaFk9kBEbD1ik0aXr6b1yF/1ea8r6nl34pHVd+q7ajdFoJPLCNcKPnmfxJ6+yrmdnKpTx\nZNK2w4US6+PW91OnTzNr9g/MmTWTiHVrqVqlCpOnTgNg0ZIlJCQksH71Ktas/An13HnWrl9fKPkw\n5UVH7+ETCOv1OZsXz6B1s0aETfrBKl3Y5B+4EneN0l4l8/gUuHkrgTWbdhRCfIXXRi5evJiE27dZ\nt3Yta1av5ty5c6xbZ/rBHzp0KM2aNWPD+vX8MHs206ZN48KFCzbMV/Eu9wd52mP/fsQkhvX6H1sW\nTaN1swYMnWwd+9Apc4i+T+y1lSr8vHCq+d+T6mRJ0eroHTaWsN5fsXnpHFo/15iwiTOs0oVNnMGV\n2KuULmUZ+8k/zzFj4XLmTxjOpiWzqVY5iIlzFj652IeNJ+z7r9i8ZDatmzUmbOJM69gnzTLVmVyx\nHzt1lh9XbWDJtDH8vHgWlSsGMm7mgicT91Na5gApqWn0mbOG0G4d2DSiJ63rKAxbuskiTVq6np4z\nfuKb19sRMfwrvuzUhr5z1gCw9+R51u09yuKQD9k8sidnoq+xKfKPQos3q72eNn0GG8Ij8PPzY8b0\naVbptNoUQvr2ZfCQUMIjNtKyVUtGDB8GwN27d/noww+pUrWK1X7Tp03F0dGRtevWs/ynFWzZvJnI\nAwes0j0Oc/v4/RdsXjwzs32cbZUubPLsPNvHY6f+NNX1qaP4efFMKlcIYNysJ1dnckrR6ugdOpKh\nfb/h558W0Pr5poSNn2qVLmzCVKJj4yhdyqsIorR046/r/DB5HKHjpvLD8nWU8/Fj8VzrYxZgWL/v\ncHXN+2ZcsxYvMHvZWvM/6WQRj+of2dGiKEqQoihJiqLsVhRlj6IokYqidM7nvi0VRSlX2DHmFBUV\nRUBAADVqmO6Wde7UiQMHDnDv3j1zmvPnz5OUlESbNm0AaN26NQkJCVy6dIldu3fTuHFjfH19zfvv\n2GG6cK1fv7757qyjoyO1atXi4sWLJCcnc+rUKTp27AjA8889h4OjI6qqPkY+DhHg70/NGqZRMZ07\ndmB/ZKRFPs6dv0BSchJtXmgNwAutWpGQcJtLly4DMHhAf9543fpPdf7CBRo1aICdnR1OTk7UrVOH\nCxcuFjhWW/h52DQ2hU4q0hjy4hhQBUPiLQw3rwKgO3sIx8Bq4Ohslda5VlMMf18jI/HWkw4zT1GX\nrhNQqgQ1/Ex3ljvVq8qBi9e4l5puTpNuyGBwx+ep6We6y9Cksi+3knUk6dI4/9dtavqXxcPFCYDG\nlX25cON24cT6mPW9VKlSjBk1Eu/MO1z169fl4sVLADRsUJ9vvvoKe3t7nJ2dqVu3DleiowslHwAH\nj50kwLc8NasFm/LyShv2HTnBvRStRbqOL71AWK/PcXDIeyTZqBkL+PT9N2weX2G2kQ0aNODrnj1z\nlHVdrly5AsAbb7xBly6mTkofHx8CK1QgOibGZvkq7uX+IE917MdPEeBTnppVKwPQpX0b9h35wyr2\nTi+1JqzX/+4be1E4eOwEAb4+1Kxm+gHZ+ZV27Dt8jHsplp3+Hdu3Jax3TxzsLUfBlS5ZkvGD++Bd\npjQA9Z99hguXbVenHxz7H5Z15l8vsu/wcevYX25D2PdfWsdeqiSj+n1LSQ/TXemm9etwOfbqE4j7\n6S1zgKizlwnwLkXNin4AdG5ej/2nL3JPl2pOozcYGPKf12hcvZIpxqoVuHEnicQULQfOXKRtvRqU\ndHfF0cGBri80ZseRM4UW7+7duyza606dOpvba4t85TovdOrU2Xxe0Gg0TJw0mdatWlvt16ZtWz77\n3+fY2dnh7u5OtWrVuHjRtteTVnX9lbaZdT2P9vH7L6zaGOu6/iyXY6/ZNMb8ijp6nAA/X2oqppG4\nXf71MvsPHc2j/rdjaN9vcXAo+skSB/fupk6DRpQrbxrp/NK/O7Jv1y95pu3a7SPe+/DTJxneP4LG\nzq7Y/ysOikcUhUNVVbW1qqqtgH8BkxVFcc3Hfj2AJ9rREh0dTWBAgPm1m5sbXl5exMTGWqQJ8Pe3\n2C8gIIDLV65Y7R8YGEhCQgKJiYnUrl2bSpVMJ069Xk9kZCS1a9c2D/k0ZmSY93N1dSU2x3c+cj5i\nYggMzJ2Pkpb5iMkjH/7+XM78YVOnzrN5fnaTxo35ZddudDodSUnJRB48SLOmTQocqy1cjjxapN9/\nP/Ze3mTczdFxkp6GUZeCfUnLYfEaNw9c6zQn5cAWq89wKOuHZ+fP8Hq/D+5t3kTj5FLYYQMQfesu\nAaU9za/dnB3xcnUmNiHRvM3DxYkXalQATENVNxw9T72K5fF0daZhJR9OxNzgr7v30Bsy+PVsDE2D\n/Qon1ses7/5+fjRsUN+8/fd9+6ldyzTUvG6dOlSoEAjAzZt/s2/fflq1aF4o+QCIjrtGoJ+P+bW7\nqyteniWIuRpvka7uM8p9P2PvwaMkp6TQvvXzto+vENvIunXrUqGCqT7dvHmT33//nZYtWwLwYtu2\nuLmZ7nSdOHGCmzdvUq9uXdvlq5iX+4M8zbFfibtGoF/2tLOs2KNzx17z/rHH3/ibj/sO41/de/LN\n0PH89feT6ayOjr1KoH+OcndzxcvTI49yt56iBeDvW56GdbKH9P9+8DDPPiCfthQdd5VAv+wRq6Zy\nzyv2vKcwV/T3o14tU750qals2rmbNs8X/nXA01zmANF//U2AdynzazcXZ7xKuBJzI8Fi24sNsqet\n/H7yAkHly+Dp5ooGMOS4VnRzdiI2x742jzc6msCAQPPrnO117nQBOdJlnRdiY2Pw9PQkKCgoz89v\n3LgJPj6mv2dycjInTpygVu3ats1Dnu3jo9R1X+rVMr1nquu/0eb5xjaNMb+uxMYR6J993Lq5ueLl\n6UlMnGXHT91aNXPvWmSuxsbg6599zvf1D+DO7QSSkxKt0taolffvDoBLF84R8tUnfPJOF6aMDuNe\ncnKhxCv+uYq+2/EJUFU1QVGUeKCaoigzgHQgA3gT8ASWAsnADKAT8IyiKK8DvwIbgOeBO8C/AXdg\nIVAKU/l9parqH4qinAc2AzdUVR3xKPHpdDqcnC1HGzg7O6PVZvd8ax+QRqfTUbp0afN2JycnNBoN\nWq0WT0/Tj1aj0cjIkSMpX748L730Evb29tSuXZslS5bw6aefcvDgQS5evEhqWtqjhG6VD2cnp1wx\nuqDV6rLTaHU4OT04r3np+tab7N7zG63atkOv19O2zQu0aP5kL86fFhoHJ4wGvcU2oz4djaPl38a9\neQdSDu3EmKaz2G64c5O0y6fRHtsDxgxKvPg2bs1f496vqws9dl26Aadcd3acHe3Rpumt0u44fYUx\nP0fi4eLE+K6mUQw1/MrwWt0q/HvSGlwdHSjn6c6CD18pnFhtWN83bvqZffv2s3SR5dDg7h9+zOkz\nZ/jv++/RtEnh/aDQpqbinKt+uDg7k5LjjueD6FJTGTv7R2aM6F8Y4T2RNvKDHj04ffo0//3Pf2ja\ntKk5bXx8PD0+/JCkpCRCQ0MtPudxFfdyf5CnOXZdaqrVsevi5IQ2n7F7lynFiy2a8NHbnfAo4c64\nHxYTMnoaC8eHFkK0lrR5xe7sRIpWd5897i9i+6/sPXiE5TMn2Cq8B9LqUnF2crTY5uLslO86k2X8\n7IWs2riV+rVr0uOd+02LtZ2nuczBNCXXOdf6Xs6OjmhT877eU2OvM3blVsZ8/DoAzZ4JZsiiCP7T\nrhkl3V1Zu/cIqenW52RbyU97nZXOyTn3Ofjh15NZ0tPT6d8vhFatWlOnTh3bBJ/JVGfyquuPVmfG\n//AjqzZuo36tGvTomq+B+Tan06XilEf91z5iXp6k1FQdJUtl1yHHzDqk02op4eH5gD2z+QdWoGnz\nVnR+533s7OyZNGIIc6dN4Jt+QworbPEP9E8e0WKmKEoQUAbTSJWvVFV9AdgHZE2qrge8p6rqRuA4\n8IGqqjFAZWCxqqrNMHWsPAt8A2xVVbUt8D8g62zpCGx51E4WMI0kSUu1vNDQ6XS4ubrmK03u91JT\nUzEajbhm7q/X6xk4aBDX//qLiRMmmBeQHTVyJMeOH6dDx45s376dunXr5rnYWP7z4WLVUaPT6XBz\ny5WPtDzy4fbgxUonTZmKv78fv+/exe+7d6HValm4aHGBY/0nM+rT0OQauqxxcMKYnv23caxQDY2L\nG2nnjlntr78ejTZqO6Sngj4d7ZFdOAU9mTsVro4OpOkNFtt06XrcnKz7hNs9E8TOPl3p92ozPlm4\nlb+TUtj9Zwy/n4tjZ5+3+a3/u7zybCUGrP2tcGK1UX1fsWo1s+fMZd4Psyhb1nKhvB/nz2XXjm1c\nunzZvH5LYXB1cSE13TIvWl0qbq75G8k0a8lqXn2xJRVy3MGzpcJuIwEWLljAr7/8YirrKVPM2319\nfdmyeTMrVqxg2rRp7N2713b5Kubl/iBPfey5jl1talq+Y68U6E+fT7tR2qskjg4OfP6fN4k6cbpA\nP7wfVZ6x61ItjoX8+GnDz8z88ScWTBqJd5lSD9/BBkyxp1tse5Q6k+X7zz5gf8RyGtWpxUe9Btky\nxDw9zWUO4OrsZNUxoktLxy1XJwXA8QsxfD51GaHdOtAocxpR81pVea9NEz6euJiPJiyibnAgHm62\nHeW6YsVPdO7Ukc6dOnL61CnScnQCZbXXucvb1K7ncQ6+z3obOaWkpNDzqy8pXbo0A3KsE2grNqvr\nn3Znf/hSGtWtxUffF80PfFcXF9Ks2stHr/+FbePalXz23ut89t7rnDt7mvQc115pmXXIJR91I0uN\n2nV478NPcXNzx8XFhTff/4BD+38vjNCfSnb2mmL/rzj4J3e0KFlrtAA/AP8F/gJGZm57B1PnC8BF\nVVXzGvebqKpq1opfcUBJ4DngM0VRdgMzM7dliSpIoJWCgiyGwCclJZGYmEiFihUt0sTGxZlfG41G\nYmNjqRwcbLV/TEwM3t7e5p7/oWFhpOp0TJk8GReX7EY+MDCQH2bPZmNEBIMHDyYuLo6qVawXDit4\nPpJN+cgclg9QqVIQsXHZc6qNRiOxcbFUrlzpgZ+9PzKS9i+1w9HRAVdXF1q3asWRo8Vz6k5RM9y+\ngV3J7B/sGicXNC6uGO7cNG9zqlwLB29/Sn0wmFIfDMbBpyIer3TDSWmAXYmSaFyyn/Kj0dhBhmXn\nR2EJ8i5J7K3soZ1JujQStWlUKJN9B+L63XvsOpu9Xknjyr6U93TnZNxNIi9c47mq/ni5uaDRaHip\nViWOXPmrUGK1RX0Pj9jIipWrWDh/LgE5prbs2r2b+HjTU6xKlChBx9deY/+ByELJB0DlCv7E5Hhq\nVlLyPRKTk6nob70wdV527T/EsnU/0/KNHrR8owcALd/oYTUVo6AKs43ctWsX8fGmOEuUKEGHDh04\nsH8/aWlprFu/HoPBVPcD/P1p0aIFB2y4aGJxL/cHeZpjrxToT8y1gsf+9+07FlOFDIYMNBoNDk/g\nKXiVKwRYTD8wxx6Q/ymS67fsZPn6TSyeOtpiekNhu2/s/vmL/Y+z5zhx5k8AHOzt6drxFf44e47E\nQh7O/zSXOUAln7IWU32SUnQkpmipUN5yOrEae51es1cx9uM3aPlsNYv3erzSnI3Dv2JZ/48p41mC\nqv62nWHftes7rN8QzvoN4bz55lvExmavYRMTE0NZb288PC1HIgQFVbJIl31eqMCD6PV6vvv2W4KD\nqxA6NAy7QljLoXKgvw3qumnNRAd7e7p2eDJ1PS+VKgZaTBNKSr5HYlIyFQL8H7DXk/fa62+bF639\nV6c3iL+afT1wLS6W0mXKUuIRbibf/Os6d29nr/FnMOixLwbrz4inyz+5o8W8Rouqqi+rqnoAmAJM\nyVy3JecjBu43Xyb32EhNZtqvMj+7taqqOSdNFmjeTaNGjYiPj+foMdPogqVLl9KyZUuL3uLg4GBK\nlSrF5s2bAYiIiMDX15egihVp3bo1UVFR5gUcFy9ZQvv27QHY+csvXLp0iVGjRuHoaDmMsefXX7Nz\n504ANm7ahI+PD35+BV/PolHDhsTHX+foseMALFm2jJYtmlvmo3JlSpXy4uctW0352LgJXx9TPh4k\nqGJF9uw19SQbDAb27d9PleDgAsf6T5YedwF7j1I4+AYB4FK3BWlXzoI+++7Kvd3ruD0/lNsLw7i9\nMAz99WiStiwiTT2Cc61mlGjzBtjZgUaDy7PPkxZ99onE3qiSD/F3kzkWbeocWbb/NC2UQFxzDMFN\nNxgYvP53LmYucht9K5HYhEQqlytFxbIlibp0zTzV6PdzcVQpVzgr4D9uff/rxg2mTJ/OrOnTKOdt\n+ZjEXbv3MOuHH8jIyMBoNLL399+pmsfTE2ylcd1aXPvrJkdOmv7Oi9dupFXThvm++xaxYAq/rV3I\nb2sW8Nsa01NAfluzIN8/XB+mMNvIXbt3M2v27Oyy3ruXqtWq4eTkxPz589m40fR0s5SUFA4fPkzV\napY/QB5HcS/3B3maY29S9xmu/fW3OfZFazfRukmDfMf+675D9Awdx73MKQpL1v9M03q1cMo1VaAw\nNK73LNeu3+DIH6cBWLx6A62aNc537H/d/JvJcxfxw9ihlCtb5uE72FDjerW59tcNjpw0LaS6eE04\nrZo2ynfsl2PiCJ0wg6Rk0yLYu/Yfwre8N555PLLVlp7mMgdoXL0S127d5eh50w2KJTsO0OrZahYj\nWoxGIwMXrGfAe6/SoJrl9dihPy/TY9xC0vV67ulSWbLjAB2fs91aVbnlbq+XLllsbq9zyjovHDtm\nuum2bOlSWrRsed8nyGT56afluLu78X3v3jaPPYuprt/MUdcjHql9vBx7ldCJM7Pr+oEofMsVfl3P\nS+P6dbj21w2Oi44hWAAAIABJREFU/nEKgMWr1tHqufzX/6LQpHkrThyJIi7mCgAbVi6j5YsvP9Jn\nbN6wlmljh6PX6zEYDGxau5JGzQpvrTzxz6R5nMf5FleZU4XWqKraMNf2k0AXIAbYAkQCc3KmVRTl\nV+A7VVWPK4ryt6qqZTO3rwGmYxrRUlJV1b6KotQE2quqOlFRlCtALVVV79vdrNNq71vYhw4dYuy4\ncWi1WgIDAxkWFobBYOB/n3/OurVrAdNTNYaGhXH3zh1KlylD6JAh5oVut23bxqzZszHo9VSvUYOh\noaG4ubnx2f/+h6qqFvNa69SpQ9jQoRw7doywYcNITU3F19eXEcOHmxcIy02Tkb/5uIcOH2bMuAnm\nfAwfOgRDRgafffEl61evAkxPYhk6bDh3796lTJnShA4aRKVKQZw5+ychAwag1+uJi7tKUJDpZB+x\nbi3Xr19n+MjR5iev1Kr1DAP7hVAiHyedr0vYdpEzAI9yZem1ZyUAPtWDuXHhChl6A5Pbvsuda7Yb\nQTFi2psF2s/BvzLuLTqicXDCcPcWyb+sRKPR4NHhY+7+ZD033LPzZ6REbUd/9RI4OOLeqguOvhXB\naCT9ejQpeyOs1nJ5GFfvgq1lcfhyPGM3R6FL1xNY2oOhnZuTYTTy+eIdrPmyEwA7Tl1h7p4TpBsM\naNDQvUUtOtSrSrohgwlboth/4Sp2Gg1lSrjS/7VmBD9iZ4v9q1/mK93j1Pd5CxYyb8FCypXL7mSx\nt7dn/epV3L17lxGjx3D2zz8xZhgJDq7M4AH9KVPm4Rfp9rcL9jSLqOOnGDVjPlpdKhX8fRjR5ysy\nDBl8EhJG+HzTVJqOH36NwZBB7LXreJcphYuzMyNDevJs9aoWn/VM2y6c/mXdI8egL1P5vu8VVht5\n9+5dRo4axdmzZzFmZBAcHMygQYMoU6YMqqoyeswYEhISMBgMtGrVil7ffZfnHVCHW5ceOb9QPMq9\noIpD7BpjxsMT3S/2mQtJ0aVS0c+HEX2+ICMjg49DhhMxz/Q0uQ4ffYvBYCD22l+ZsTsxqu9X1KoW\nzPi5S/hl3yHs7ewIrhjAwK8+pPwj/og2OhTsh0rUsT8YNX0OWm0qFfx9GRHyLRkZBj7pPZjwH02P\nS+7Y/fPMco/Hu2xpXJycGdn/Ww4ePcGcpastYrW3tzPvl//gC1ruJxk1bS5anS4z9m9MdabPEMIX\nmh653vGDLzPL/Xpm7E6M7PcttatXZdrCZWzfvQ8j4OHuTv+vPn70hWU1j36PsViUOZBx4cgj7wOm\nzpLRK7agTU2nQrnSDO/RCUOGkc8mLWF92BccvxhLt9HzrUa5jPn4dZRAH4Yv3cS+Uxews9Pwnxeb\n8d6LTe/zTfenb9gp32m3b9vG7Nmz0OsN1KhRnSGhQ3Fzc+PUyZPMnDmDmbNMj0o+fOgQY8eNRZd5\nXhgaNoyyZcuyZ89uJk+ahE6n4++//yYgIIBy5crxw5y5dOrYAa1WazGF98V27fjii/uf950SLj9y\nfqOOn2TU9PnZdb1vT1Nd7zuU8AWmxyN37NEzu66XKY2LsxMjQ77OrOvL2b5nP0ajEY8SmXW9xqN3\n9BsdH3+KT9SxE4yeMiszL36M6P89howMPu3Vnw2L5wDQ6b+fmPJyNR7vsmVMeRnQm9o1817wNz+u\nUPCOyb2/7mDZgh8wGAxUqVadnn0H4ermhnrmFEvnzWbYxOncTrhFv68+ASAuJhpf/wDs7e0ZPnkW\nJTw8mTVhNGdOncBOo6FGrTp83LMX7vns7KpazqN4zF0pJLvqNSn2HQgvHDtY5H+D/28dLZ8AXwMX\nMS1oOx3TE4nm5+hoGQL8B+gI7Mmjo+UI8COm9V7sgZ6qqh5+3I6W4i6/HS3FUWF0tDwpBe1oKQ4K\n2tFSHOS3o6U4KmhHS3HwoI6W4q6gHS3i8RS0o6U4KGhHS7HwFJd7QTpaiouCdrQUB4/S0VLcFKSj\npbiwRUdLUXmcjpaiJh0tRa84dLT8Iyebqap6BWiYx/Y5mEawZFmf+d+GOdIMBYZmviybY/sbOfZ7\nPY/PDipwwEIIIYQQQgghhPhH+Ed2tAghhBBCCCGEEMK2NPZP78jAJ0lKSQghhBBCCCGEEMJGpKNF\nCCGEEEIIIYQQwkZk6pAQQgghhBBCCCEeys6+yNeZfSrIiBYhhBBCCCGEEEIIG5GOFiGEEEIIIYQQ\nQggbkalDQgghhBBCCCGEeCiNnUwdyg8Z0SKEEEIIIYQQQghhI9LRIoQQQgghhBBCCGEj0tEihBBC\nCCGEEEIIYSOyRosQQgghhBBCCCEeys5exmrkh5SSEEIIIYQQQgghhI1IR4sQQgghhBBCCCGEjcjU\nISGEEEIIIYQQQjyUxl4e75wfMqJFCCGEEEIIIYQQwkako0UIIYQQQgghhBDCRmTqkBBCCCGEEEII\nIR5Kpg7lj3S0PEEZPL2V8twdQ1GHUGAjpr1Z1CEU2ICvVhd1CAU24/KGog6hwNLtnt6mUVe6clGH\nUGAxd9OLOoQCq+JSsqhDKDCji0dRh1Bgxqf4WLVLTSrqEAoszfnpre8GY1FHUHDunpeLOoQCS9Dq\nizqEAvN5ituZeIeyRR1CgWXoM4o6BCEei0wdEkIIIYQQQgghhLCRp7eLVgghhBBCCCGEEE+Mnb2M\n1cgPKSUhhBBCCCGEEEIIG5GOFiGEEEIIIYQQQggbkalDQgghhBBCCCGEeCh56lD+yIgWIYQQQggh\nhBBCCBuRjhYhhBBCCCGEEEIIG5GOFiGEEEIIIYQQQggbkTVahBBCCCGEEEII8VB2drJGS37IiBYh\nhBBCCCGEEEIIG5GOFiGEEEIIIYQQQggbkalDQgghhBBCCCGEeCiNvYzVyA8pJSGEEEIIIYQQQggb\nkY4WIYQQQgghhBBCCBuRqUPFyNatW5g3dy56vZ7gKlUIDR2Kh4eHVbqoqINMmjiRlJQUfH39GBoW\nRvny5QFYtnQpa9euISMjg3r169O//wAcHR25desWI4YP4+LFi2g0GkJC+tG0WTMADh8+zJTJk0hO\nTsbFxYXve/ehQYMGNsvXvl+3s27pQgx6PYGVKvO/3oNwK1HCKt3hfb+x6sc5pKen4+HpyUffhlCh\nUjAAd28nMG3kEG7EX2Pq0rU2i+1BHPyDcX/+VTROzhgSb3Pvl1Vk3LubZ1r7Mr6UfOtrEiPmoL96\nCefqDXFr0QFjSpI5je6PfehO7n8isT+MnYMDnUf3pV2vjwkJaMqdq9eLOiSzyOOnGDd3CSnaVPzK\nlWVEr//h413GIo3RaGTBmo1MWbiChWMH06BWdfN7F6LjGDBhFncSk/DyKMGI7z+nSsWAQon1YFQU\nEzOPRT9fX8JyHItZVFVlxIgR3L5zh1JeXgwcOJBq1aoBsGXrVuZmHvNVqlRhaGgoHh4e6PV6xo0f\nT2RkJBkZGTRu3Jh+ISE4ONi2yd62dSvz52W2OcFVGJz5/blFRUUxedJEtCkp+Pr6MmRodj7nzpnD\nli2bMWZkoFSvzoCBg/Dw8CApKYmRw4ejnlMxZmTQ7qWX+fyLL2waf057f9nO6iXzMej1VKgUzJd9\nB+OeRzuj1+tZ8sM0IlYtZ+7qTZQtZ8qHwWDgx5mTOXpwPxqNHUrNWnz0dW9c3dxsGufBI8eYMH02\nKVotvuXLM2xAH3zKeVukUc9fZNj4ydy5exevkiUZ1PsblCqmtnDe4uWEb9mORqOhcsUKDPz+a8qW\nKQ3A6T/P8f2gMBrXr8vQft/bNG6Ag1GHmDB5KlqtqR6EDRmET+76fu4cw0eN4c6dO3h5eTGofwjV\nqlYFYM26DSz76ScMhgz8/HwJHTTAav/v+oRw584dFsyZbePYo5g4aXL2sTo0NI9j9RwjRo7MPlYH\n9DcfqykpKQwbPoJt27dz9PAh8z6zZs9mxcpVeHl5mbf1/OpL2rZpY7vYDx9lwtSZpjrjU55hg0Lw\nKVfOMvbzFxg2diJ37tzFy6skg/r0QqlqqjMXL19h0LDR3L57F6+Sngwf1I/gSkEW+3/XbzC379xl\n4awpNos7iy3amYSEBAb278fVa9cIj9hosd/qVatYvOhHAJo2a0afviE4OjraJPbt27ayIEfsg4aE\nUiKP2A9FRTF1ctZ1mS+DQrNjnzNrJjt2bCcjIwNFqU6/gQPx8PDk77//ZvSI4Vy5chl7O3v+9dpr\ndOv+gU3izi3ylMr4JRtI0aXiV7Y0wz9/D58ypSzSHP3zEmMXr+OeVoeLkxN9u3WhYc0qFmmWbd3D\nyAVrOL1qWqHEeT+/7tjGsh/no9frqVQ5mO8HDKZECeu/g16fztyZ01jz0zJWhG/Gu1z2MX47IYGR\noQO5fu0qS9aEF3rMkUdPMH7WAlK0WvzKl2N432/wKVfWIo3RaGThynVMmbuYBZNG0uDZZ8zvrd60\nlSVrIsgwZODnU46w3j2t9i9su3ZsY3lmuQdllrv7fcp93sxprP1pGcvzKPfRmeW+6AmUe5bfdm5n\n1eLMa4LKwfQMuf81waLZ0whfuZwFa7OvCQDO/HGcmeNHkZaairePL98NCqNMWW+rz/j/yM5enjqU\nHzKipZiIj49nzJgxTJs+gw3hEfj5+TFjuvWJTKtNIaRvXwYPCSU8YiMtW7VkxPBhAPzxxx8sX76M\nRYsWs35DOElJSfy0fDkAY8eMJiAwkPCIjYwbP4EBA/pz7949dDodvb/vRb/+A1i/IZxPPv2Mvn16\nYzQabZKvv/+6zsJpE+g3ahKTF6/G28ePFfNnWaVLuHmDmWPC6DkgjEk/ruT5ti8zd+JoAJIT7xL6\n7f/MnS5PhIMjHi+/T/KuNdxZOpb0K2dwf6HLfRJrcG/dhYwcnSoAaZdOc2fZOPO/4tLJAvB5+FxS\nk1OKOgwrKTod34+cwrBvPmXLgsm0btqAoVPnWaUbOnUe0XHxlPbytNhuMGTwddgEPnqrA9t+nMp7\nnV5h7dZfCydWrZa+ffsSOmQIGyMiaNmqFcOGD7dK1zckhO7du7MxIoIePXrQr39/IPuYnzF9OhHh\n4fj5+TFt+nQAli1bxpUrV1izejXr1q7lwoULhIfb9gIlPj6esWPGMGXadNZtCMfXz4+ZM6ZbpdNq\ntfQP6cugwUNYHx5Bi5atGDnClM+dO3awY8d2lixdxtr1G9CgMf/gmTplMmW9y7Ju/QYWL13K1i2b\n+X3vXpvmIcvNv64zb8o4Bo2Zwoylaynn48uyeTPzTDuqfy9cXK07T37ZHMGlcyqTF/zE1EUrSU9P\nZ93yRTaNM0Wrpc/g4YSG9GLTisW0bt6MYeMmWaXrPWQ4H7z3NptWLObD998hZOgoAPZHHWb9z1tZ\nPnc6EcsXUrFCAOOnmzokDh07weBR46hds7rV59ks9v4DCR00gI3r19KqRQuGjxxtla5Pv4F88N//\nsHH9Wnp070bIgMEAnDp9hlk/zGHOzBlErFtN1SpVmDzVsr79tvd3zpw5Wyix9w3pR+jgQWwM30DL\nli0ZNmKEVbq+/ULo3r0bG8M30OODD+g3YKD5vf92746vr0+en9/17bcIX7/O/M+WnSwpWi19Bg0l\ntH8fNq1eRuvmzzFszESrdL0HDuWD999h0+plfPif9wgJNV0XGAwGvgkZRI//vMuWtT/x7puvszZ8\nk8W+v+07wOmzqs1izskW7czdu3f5+KMPqVKlqtV+x44dY9nSJSxespT14RHcu3ePEyeO2yT26/Hx\njB8zhslTp7NmvSn2WfeJfWC/vgwYNIS1G0yxj86MfdvWLRw8GMmS5StYvW4DGRkZLJw/H4ApkyZQ\noWJF1qwPZ/6ixURsWE/UwUibxJ5Tii6V3pN/JOzTd9k8ZTCtG9QibO5KizRp6el8NW4O377bgY2T\nBvLV2/+m95QfLdLcvH2XNTuf/HXMX9fjmT5xLCMnTGHRynWU9/Vlwey82/dBfXrhmkf7nnj3Lt99\n/jGVgqvksZftpWh19A4bS1jvr9i8dA6tn2tM2MQZVunCJs7gSuxVSpcqabH95J/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Gi2wX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AgeyCO8Z0SFIjxaBAKBQCAQCAQCgUAgEAjKCGFoEQgEAoFAIBAIBAKBQCAoI4ShRSAQCAQC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98iXGdPueEp5eKLXrcyV9WFF+ebh99T+6jJtN+HcfZeyrIQG+dPusdUabDu+34Plv+6DR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\nUQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2_BIVBZAOMs"
      },
      "source": [
        "## 5: Explore The Data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JAyZt0XcANqa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bc61af8a-c64f-4699-a1f0-a7ced16eaed2"
      },
      "source": [
        "# Explore how many churn data points we have.\n",
        "print(len(data['Churn']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7043\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0WP1moGfjhg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "20442d18-41c7-42bf-c3ab-697b0b756422"
      },
      "source": [
        "# Explore how many customers in this dataset have churned. Is this dataset 50% as the team suggests is the overall customer churn rate?  \n",
        "data['Churn'].value_counts()\n",
        "# We see this dataset actually has less than the overall 50% churn rate of the entire company reported data (it's actually 26.54% that have churned."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    5174\n",
              "1    1869\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_X08xUR8FRGH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "outputId": "a76bbe37-dfbf-460f-a555-e9f5caf7f87b"
      },
      "source": [
        "# This creates a bar graph of churn (Yes vs. No) so we can check how the data is balanced.\n",
        "data['Churn'].value_counts().plot(kind = 'bar', title = 'Bar Graph of Non-Churners vs Churners by Count (Churn is a 1)', color = 'blue', align = 'center')\n",
        "plt.show()\n",
        "# The dataset does not have a huge imbalance which is good news! But also we clearly see it does not have the 50% as we would have thought. "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pR+AxYHpEjAZOAHYFpgDHRMR44EBgnqQdgNOA01u9E2ZmNjjL2+0zBZiRX19HCvxtgdmS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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_KTJ2EeUHVGM"
      },
      "source": [
        "Explore some contingencies on how some features relate to churn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QO8tgNqvHUve",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "78e3f66a-ceb3-486a-dbb2-6556ab89ef20"
      },
      "source": [
        "# Creates initial contingency table between Churn and gender. Male is 0, Female is 1.\n",
        "gender_churn_contingency = pd.crosstab(data[\"gender\"], data[\"Churn\"])\n",
        "display(gender_churn_contingency)\n",
        "# Male and females churn at about the same rate, so not much to see here. Let's keep moving."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Churn</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2630</td>\n",
              "      <td>930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2544</td>\n",
              "      <td>939</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Churn      0    1\n",
              "gender           \n",
              "0       2630  930\n",
              "1       2544  939"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TeatExD7H7fP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "outputId": "3dbe58d9-ef7b-4ce9-d3c0-c84d3c5c729c"
      },
      "source": [
        "# Check the data health. The sections should all be completely black indicating the data is complete.\n",
        "ms.matrix(data)\n",
        "# It looks good."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4f435b0588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bngUTru9yGTANsL2kk6JWVLYmiv8ZRwveKWmjdrX3hxIu7j4Qi3wnlW2VPUpv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\ny+Z3hFvPd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            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_B7gx95IQKf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "44a1115e-fdf8-408c-f202-71e3c0bcc0a5"
      },
      "source": [
        "# Explore the relationship between instances of Tech Support and Churn. \n",
        "# Stacked Bar of Tech Support and Churn.\n",
        "tech_support_churn = pd.crosstab(data['TechSupport'], data['Churn'])\n",
        "tech_support_churn.plot(kind = 'bar', stacked = True)\n",
        "plt.ylabel('Count')\n",
        "plt.xlabel('Tech Support Count')\n",
        "plt.title('Churn Rate Relative to Uses of Tech Support (Churned is a 1)')\n",
        "plt.show()\n",
        "# We can see that non-churners use tech support more often than customers that end up churning.\n",
        "# So let's explore some ways to get people to use Tech Support more often so they cancel (churn) less. You can see notes for this at the bottom. \n",
        "# Also, tech support in this data is just a Y/N. It would be useful in future to include how many tech support calls by customer so we could analyze how the number of tech support calls relates to churn."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZiIisILklCHdfGHf4WR3cfUF8/TGwLtAdqM4Ms1S5u9cAtWbWLq94RURkSbn2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\n7vR7OXyVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8xHAhIJecrR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "698f8d9c-e664-4c2b-d651-c299a47fa6b6"
      },
      "source": [
        "# Churn rate relative to tenure.\n",
        "# Stacked bar of tenure and churn.\n",
        "tenure_churn = pd.crosstab(data['tenure'], data['Churn'])\n",
        "tenure_churn.plot(kind = 'bar', stacked = True)\n",
        "plt.ylabel('Count')\n",
        "plt.xlabel('Tenure of Subscription')\n",
        "plt.title('Churn Rate Relative to Tenure of Subscription (Churned is a 1)')\n",
        "plt.show()\n",
        "# We can clearly see the longer a customer stays as a subscriber, the less they are likely to churn!"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T9RSG3z+S/SaUfTNAEZGKW7hwIWecMZKDDjqUa665kdtuuw2AceOuaeOSlaeqPQUzO5i4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\nQdlwnAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onWnkXWVbFdA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "929df6a5-c006-4245-de23-02a804344d89"
      },
      "source": [
        "# Distribution of features.\n",
        "features = ['gender', 'SeniorCitizen','Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges']\n",
        "data[features].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.494534</td>\n",
              "      <td>0.162147</td>\n",
              "      <td>0.481755</td>\n",
              "      <td>0.298026</td>\n",
              "      <td>32.371149</td>\n",
              "      <td>0.901888</td>\n",
              "      <td>0.421269</td>\n",
              "      <td>1.222206</td>\n",
              "      <td>0.286100</td>\n",
              "      <td>0.344314</td>\n",
              "      <td>0.343320</td>\n",
              "      <td>0.289649</td>\n",
              "      <td>0.383785</td>\n",
              "      <td>0.387761</td>\n",
              "      <td>0.687491</td>\n",
              "      <td>0.591793</td>\n",
              "      <td>1.313503</td>\n",
              "      <td>64.697004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.500006</td>\n",
              "      <td>0.368612</td>\n",
              "      <td>0.499702</td>\n",
              "      <td>0.457424</td>\n",
              "      <td>24.559481</td>\n",
              "      <td>0.297487</td>\n",
              "      <td>0.493798</td>\n",
              "      <td>0.779535</td>\n",
              "      <td>0.451969</td>\n",
              "      <td>0.475178</td>\n",
              "      <td>0.474851</td>\n",
              "      <td>0.453632</td>\n",
              "      <td>0.486341</td>\n",
              "      <td>0.487274</td>\n",
              "      <td>0.832727</td>\n",
              "      <td>0.491537</td>\n",
              "      <td>1.149799</td>\n",
              "      <td>30.171186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>35.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>70.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>89.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>118.750000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            gender  SeniorCitizen      Partner   Dependents       tenure  \\\n",
              "count  7043.000000    7043.000000  7043.000000  7043.000000  7043.000000   \n",
              "mean      0.494534       0.162147     0.481755     0.298026    32.371149   \n",
              "std       0.500006       0.368612     0.499702     0.457424    24.559481   \n",
              "min       0.000000       0.000000     0.000000     0.000000     0.000000   \n",
              "25%       0.000000       0.000000     0.000000     0.000000     9.000000   \n",
              "50%       0.000000       0.000000     0.000000     0.000000    29.000000   \n",
              "75%       1.000000       0.000000     1.000000     1.000000    55.000000   \n",
              "max       1.000000       1.000000     1.000000     1.000000    72.000000   \n",
              "\n",
              "       PhoneService  MultipleLines  InternetService  OnlineSecurity  \\\n",
              "count   7043.000000    7043.000000      7043.000000     7043.000000   \n",
              "mean       0.901888       0.421269         1.222206        0.286100   \n",
              "std        0.297487       0.493798         0.779535        0.451969   \n",
              "min        0.000000       0.000000         0.000000        0.000000   \n",
              "25%        1.000000       0.000000         1.000000        0.000000   \n",
              "50%        1.000000       0.000000         1.000000        0.000000   \n",
              "75%        1.000000       1.000000         2.000000        1.000000   \n",
              "max        1.000000       1.000000         2.000000        1.000000   \n",
              "\n",
              "       OnlineBackup  DeviceProtection  TechSupport  StreamingTV  \\\n",
              "count   7043.000000       7043.000000  7043.000000  7043.000000   \n",
              "mean       0.344314          0.343320     0.289649     0.383785   \n",
              "std        0.475178          0.474851     0.453632     0.486341   \n",
              "min        0.000000          0.000000     0.000000     0.000000   \n",
              "25%        0.000000          0.000000     0.000000     0.000000   \n",
              "50%        0.000000          0.000000     0.000000     0.000000   \n",
              "75%        1.000000          1.000000     1.000000     1.000000   \n",
              "max        1.000000          1.000000     1.000000     1.000000   \n",
              "\n",
              "       StreamingMovies     Contract  PaperlessBilling  PaymentMethod  \\\n",
              "count      7043.000000  7043.000000       7043.000000    7043.000000   \n",
              "mean          0.387761     0.687491          0.591793       1.313503   \n",
              "std           0.487274     0.832727          0.491537       1.149799   \n",
              "min           0.000000     0.000000          0.000000       0.000000   \n",
              "25%           0.000000     0.000000          0.000000       0.000000   \n",
              "50%           0.000000     0.000000          1.000000       1.000000   \n",
              "75%           1.000000     1.000000          1.000000       2.000000   \n",
              "max           1.000000     2.000000          1.000000       3.000000   \n",
              "\n",
              "       MonthlyCharges  \n",
              "count     7043.000000  \n",
              "mean        64.697004  \n",
              "std         30.171186  \n",
              "min          0.000000  \n",
              "25%         35.450000  \n",
              "50%         70.350000  \n",
              "75%         89.850000  \n",
              "max        118.750000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJ2CT6K0dNJq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "f1b6dac2-5d71-4fb5-8589-d14aa66d0415"
      },
      "source": [
        "# Plot the distribution of observations for tenure.\n",
        "sns.distplot(data['tenure']);\n",
        "# It shows the max tenure is 70. This must be when the data history ends. We'll account for this in our analysis."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a3gw8A6CqR0TELyJ5qtohIguAVlWtARCR7U75wKW2AbYAf+7s9zng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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_UTsEzPeiQUt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "24977427-0405-44aa-a4b4-e7c1da00c2e4"
      },
      "source": [
        "# Does how a customer pays have to do with their churn?\n",
        "_, axes = plt.subplots(1, 2, sharey=True, figsize=(10, 4)) \n",
        "sns.countplot(x='PaperlessBilling', hue='Churn',\n",
        "              data=data, ax=axes[0]);\n",
        "sns.countplot(x='PaymentMethod', hue='Churn',\n",
        "              data=data, ax=axes[1]);\n",
        "# We can see that customers that use paperless billing are much more likely to churn (0 = don't have paperless billing). That seems backwards I would go check that data with the team.\n",
        "# We can see that customers that have the 0 payment method (electronic check) are much more likely to churn. Let's discourage that option."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQXpbroASVLz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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jd4Fpqwzlebv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "25fb6f2d-7150-40be-ae97-5dc62ade90ad"
      },
      "source": [
        "# See if the other products they have from this company has to do with their churn.\n",
        "_, axes = plt.subplots(1, 2, sharey=True, figsize=(10, 4)) \n",
        "sns.countplot(x='PhoneService', hue='Churn',\n",
        "              data=data, ax=axes[0]);\n",
        "sns.countplot(x='InternetService', hue='Churn',\n",
        "              data=data, ax=axes[1]);\n",
        "# If they don't have Phone Service, they are more likely to churn.\n",
        "# If they don't have Internet Service, they are more likely to churn. Those customers with the highest Internet Service are least likely to churn.\n",
        "# Conclusion: This makes sense. Customers with other products from the company, and premium products, churn less.\n",
        "# Offer customers these additional products, perhaps even at a deep discount, so they take them and are less likely to churn. "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Za0jp-Npy0ps"
      },
      "source": [
        "## 6:  Prepare the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1INgdQZMy0pt"
      },
      "source": [
        "# Splitting the data for testing and training."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OduTMX_ay0py"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data.drop('Churn',axis=1), \n",
        "                                                    data['Churn'], test_size=0.30, \n",
        "                                                    random_state=101)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhI4K6nby0p1"
      },
      "source": [
        "train=pd.concat([X_train,y_train],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ejwbdo3ny0p8"
      },
      "source": [
        "# Function to estimate the best value of n_estimators and fit the model with the given data."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDrWVKRSy0p_"
      },
      "source": [
        "def modelfit(alg, dtrain, predictors,useTrainCV=True, cv_folds=5, early_stopping_rounds=50):\n",
        "    \n",
        "    if useTrainCV:\n",
        "        #to get the parameters of xgboost\n",
        "        xgb_param = alg.get_xgb_params() \n",
        "        \n",
        "        #to convert into a datastructure internally used by xgboost for training efficiency \n",
        "        # and speed\n",
        "        xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values)\n",
        "        \n",
        "        #xgb.cv is used to find the number of estimators required for the parameters \n",
        "        # which are set\n",
        "        cvresult = xgb.cv(xgb_param, xgtrain, \n",
        "                          num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n",
        "                        metrics='auc', early_stopping_rounds=early_stopping_rounds)\n",
        "        \n",
        "        #setting the n_estimators parameter using set_params\n",
        "        alg.set_params(n_estimators=cvresult.shape[0])\n",
        "        \n",
        "        print(alg.get_xgb_params())\n",
        "    \n",
        "    #Fit the algorithm on the data\n",
        "    alg.fit(dtrain[predictors], dtrain['Churn'],eval_metric='auc')\n",
        "    \n",
        "    return alg"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AsILrXIay0qC"
      },
      "source": [
        "# Function to get the accuracy of the model on the test data given the features considered."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9WG0lNYy0qF"
      },
      "source": [
        "def get_accuracy(alg,predictors):\n",
        "    dtrain_predictions = alg.predict(X_test[predictors])\n",
        "    dtrain_predprob = alg.predict_proba(X_test[predictors])[:,1]\n",
        "    print (\"\\nModel Report\")\n",
        "    print (\"Accuracy : %.4g\" % metrics.accuracy_score(y_test.values, \n",
        "                                                      dtrain_predictions))\n",
        "    print (\"AUC Score (Train): %f\" % metrics.roc_auc_score(y_test.values, \n",
        "                                                           dtrain_predprob))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBevLNUWy0qH"
      },
      "source": [
        "# Function to get the feature importances based on the model fit."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzV_bz3Hy0qK"
      },
      "source": [
        "def get_feature_importances(alg):\n",
        "    #to get the feature importances based on xgboost we use fscore\n",
        "    feat_imp = pd.Series(alg._Booster.get_fscore()).sort_values(ascending=False)\n",
        "    print(feat_imp)\n",
        "    \n",
        "    #this shows the feature importances on a bar chart\n",
        "    feat_imp.plot(kind='bar', title='Feature Importances')\n",
        "    plt.ylabel('Feature Importance Score')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-96Qa11y0qQ"
      },
      "source": [
        "target = 'Churn'\n",
        "IDcol = 'customerID'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xn7ABOQky0qV"
      },
      "source": [
        "## 7: Model Selection, Predictions, and Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtqV9eggy0qW"
      },
      "source": [
        "# To return the XGBClassifier object based on the values of the features."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9kBqFmhe0kWQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "58f586ad-a11a-4221-d49c-d5f166abf441"
      },
      "source": [
        "!pip install xgboost\n",
        "# XGBoost converts weak learners to strong learners through an ensemble method. \n",
        "# Unlike bagging, in the classical boosting the subset creation is not random and depends upon the performance of the previous models."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.6/dist-packages (0.7.post4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.14.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.1.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "swdjCkipy0qa"
      },
      "source": [
        "def XgbClass(learning_rate =0.1,n_estimators=1000,max_depth=5,min_child_weight=1,\n",
        "             gamma=0,subsample=0.8,colsample_bytree=0.8):\n",
        "    xgb1 = XGBClassifier(learning_rate=learning_rate,\n",
        "                         n_estimators=n_estimators,\n",
        "                         max_depth=max_depth,\n",
        "                         min_child_weight=min_child_weight,\n",
        "                         gamma=gamma,\n",
        "                         subsample=subsample,\n",
        "                         colsample_bytree=colsample_bytree)\n",
        "    return xgb1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m837XZX5y0qd"
      },
      "source": [
        "# Function to return the list of predictors."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dQWyI57y0qf"
      },
      "source": [
        "# These are the initial parameters before tuning.\n",
        "def drop_features(l):\n",
        "    return [x for x in train.columns if x not in l]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jgoA5DT7y0qj"
      },
      "source": [
        "### First Prediction: Use of initial parameters and without feature engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qjz-FBOZ00Hn"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "import xgboost as xgb"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTNEx7Jfy0qj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "outputId": "6684e365-08e3-406f-8d54-adb6636c2eca"
      },
      "source": [
        "predictors = drop_features([target, IDcol])\n",
        "xgb1=XgbClass()\n",
        "first_model=modelfit(xgb1, train, predictors)\n",
        "xgb1.fit(train[predictors],train['Churn'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'base_score': 0.5, 'booster': 'gbtree', 'colsample_bylevel': 1, 'colsample_bytree': 0.8, 'gamma': 0, 'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 5, 'min_child_weight': 1, 'missing': None, 'n_estimators': 33, 'nthread': 1, 'objective': 'binary:logistic', 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1, 'seed': 0, 'silent': 1, 'subsample': 0.8}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "       colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
              "       max_depth=5, min_child_weight=1, missing=None, n_estimators=33,\n",
              "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
              "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "       silent=True, subsample=0.8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpuBOp0Yy0qm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "c1df232f-f1c1-4029-8e47-b159d9d7f1fa"
      },
      "source": [
        "get_accuracy(first_model,predictors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model Report\n",
            "Accuracy : 0.8022\n",
            "AUC Score (Train): 0.849018\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2jOlGPtm07C2"
      },
      "source": [
        "**Accuracy** is the proportion of true positives and negatives in the whole data set. It determines if a value is accurate compare it to the accepted value; the nearness of a calculation to the true value.\n",
        "\n",
        "**AUC** (area under the ROC receiver operating characteristic curve) measures how true positive rate (recall) and false positive rate trade off, so in that sense it is already measuring something else. More importantly, AUC is not a function of threshold. It is an evaluation of the classifier as threshold varies over all possible values. It is in a sense a broader metric, testing the quality of the internal value that the classifier generates and then compares to a threshold. \n",
        "\n",
        "**Accuracy vs AUC**: The accuracy depends on the threshold chosen, whereas the AUC considers all possible thresholds. Because of this it is often preferred as it provides a “broader” view of the performance of the classifier, but they still measure different things and as such using one or the other is problem-dependent."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LTOwEH9iy0qp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "outputId": "07ef1781-3170-493d-dfe3-ee190db8ff1f"
      },
      "source": [
        "get_feature_importances(first_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MonthlyCharges      270\n",
            "tenure              195\n",
            "PaymentMethod        58\n",
            "Contract             57\n",
            "InternetService      42\n",
            "OnlineBackup         39\n",
            "StreamingTV          26\n",
            "PaperlessBilling     26\n",
            "OnlineSecurity       25\n",
            "SeniorCitizen        24\n",
            "TechSupport          23\n",
            "Partner              20\n",
            "PhoneService         18\n",
            "MultipleLines        18\n",
            "StreamingMovies      16\n",
            "gender               16\n",
            "Dependents           16\n",
            "DeviceProtection     12\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oYgy+D2wCnAw8DMwELhnsIEkL4l3Sbml46etm9uuG9x2Nd1CbDkyWdJWZvVToDIIgCIKm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\nBEEQAP8Pv6diU5rpIyUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7tki9U_y0qt"
      },
      "source": [
        "### Second Prediction: Using intial Parameters and removing features of least importances"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8ICSpTry0qt"
      },
      "source": [
        "# Model after removing the features of least importance."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwzYOgb7y0qx"
      },
      "source": [
        "dropl=['DeviceProtection','Dependents','Dependents','gender','StreamingMovies','MultipleLines']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-MObariy0q0"
      },
      "source": [
        "dropl_first=dropl+[target,IDcol]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKroiJ2hy0q2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "4f53bb18-d7c7-4650-8173-eba50ab25962"
      },
      "source": [
        "# These are the initial parameters before tuning.\n",
        "predictors = drop_features(dropl_first)\n",
        "xgb1 = XgbClass()\n",
        "second_model=modelfit(xgb1, train, predictors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'base_score': 0.5, 'booster': 'gbtree', 'colsample_bylevel': 1, 'colsample_bytree': 0.8, 'gamma': 0, 'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 5, 'min_child_weight': 1, 'missing': None, 'n_estimators': 35, 'nthread': 1, 'objective': 'binary:logistic', 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1, 'seed': 0, 'silent': 1, 'subsample': 0.8}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ny8g14-y0q5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "2c65b5b2-9eac-44bb-eb8d-2b158b92eef1"
      },
      "source": [
        "get_accuracy(second_model,predictors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model Report\n",
            "Accuracy : 0.8069\n",
            "AUC Score (Train): 0.847366\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qIp7Hsey0q8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "6148a24b-7a59-4676-8c8b-93127f1295cf"
      },
      "source": [
        "get_feature_importances(second_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MonthlyCharges      289\n",
            "tenure              190\n",
            "PaymentMethod        87\n",
            "Contract             63\n",
            "InternetService      57\n",
            "SeniorCitizen        38\n",
            "PaperlessBilling     35\n",
            "StreamingTV          30\n",
            "OnlineSecurity       29\n",
            "TechSupport          28\n",
            "OnlineBackup         28\n",
            "PhoneService         26\n",
            "Partner              26\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CZUDq9zxJ0rtJJ5x/kbpBSpsvIn7auBMREyR9qYJy6vi93ZS/qrYdcBDwQ1Kz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\nmJmZg4GZmTkYmJkZ8P86orOEthVy2AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXh08Urby0q-"
      },
      "source": [
        "### Third Prediction: Again removing the features of least importance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luJXI3key0q-"
      },
      "source": [
        "dropl1=dropl+['Partner','PhoneService','OnlineBackup','TechSupport','OnlineSecurity']\n",
        "dropl_second=dropl_first+['Partner','PhoneService','OnlineBackup','TechSupport','OnlineSecurity']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOO6UZLsy0rB"
      },
      "source": [
        "predictors=drop_features(dropl_second)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5g3UZTFy0rD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "52937477-816e-4e1f-9931-39f43e7699da"
      },
      "source": [
        "xgb1=XgbClass()\n",
        "third_model=modelfit(xgb1,train,predictors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'base_score': 0.5, 'booster': 'gbtree', 'colsample_bylevel': 1, 'colsample_bytree': 0.8, 'gamma': 0, 'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 5, 'min_child_weight': 1, 'missing': None, 'n_estimators': 50, 'nthread': 1, 'objective': 'binary:logistic', 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1, 'seed': 0, 'silent': 1, 'subsample': 0.8}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRB8Q3wWy0rG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "da316409-2da7-493a-a11e-b2cb92f5d985"
      },
      "source": [
        "get_accuracy(third_model,predictors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model Report\n",
            "Accuracy : 0.7984\n",
            "AUC Score (Train): 0.843116\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBMTnnlPy0rO"
      },
      "source": [
        "## 8: Predict on New Cases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t21bqh6_y0rQ"
      },
      "source": [
        "# Function stores the result in required csv file and features. I removed this section for now as the csv files are not yet saving correctly. See prototype version of notebook for details."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Alep7Q3Ky0rr"
      },
      "source": [
        "## 9: Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyqfjclly0ru"
      },
      "source": [
        "# Tune max_depth and min_child_weight."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3j-9y92iy0ry",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "f27079eb-d7c5-4197-a013-e97027062eb0"
      },
      "source": [
        "predictors = drop_features(dropl_first)\n",
        "predictors"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['SeniorCitizen',\n",
              " 'Partner',\n",
              " 'tenure',\n",
              " 'PhoneService',\n",
              " 'InternetService',\n",
              " 'OnlineSecurity',\n",
              " 'OnlineBackup',\n",
              " 'TechSupport',\n",
              " 'StreamingTV',\n",
              " 'Contract',\n",
              " 'PaperlessBilling',\n",
              " 'PaymentMethod',\n",
              " 'MonthlyCharges']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6L3sYX43y0r2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2453
        },
        "outputId": "d561721a-91a5-4a25-f402-094aa979f568"
      },
      "source": [
        "param_test1 = {\n",
        " 'max_depth':list(range(5,10,1)),\n",
        " 'min_child_weight':list(range(5,10,1))\n",
        "}\n",
        "gsearch1 = GridSearchCV(estimator=XgbClass(n_estimators=48),param_grid =param_test1,\n",
        "                        scoring='roc_auc',n_jobs=-1,iid=False, cv=5, verbose=3)\n",
        "gsearch1.fit(train[predictors],train[target])\n",
        "gsearch1.cv_results_, gsearch1.best_params_, gsearch1.best_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  28 tasks      | elapsed:    5.5s\n",
            "[Parallel(n_jobs=-1)]: Done 125 out of 125 | elapsed:   21.5s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'mean_fit_time': array([0.20724802, 0.20932374, 0.20880656, 0.20871305, 0.21010675,\n",
              "         0.24907684, 0.25009632, 0.24860916, 0.24591808, 0.24454684,\n",
              "         0.28334703, 0.28474541, 0.28169541, 0.28036094, 0.27825117,\n",
              "         0.31879997, 0.31600304, 0.31035895, 0.30742917, 0.3038981 ,\n",
              "         0.34530797, 0.33879218, 0.3390099 , 0.32813511, 0.31488495]),\n",
              "  'mean_score_time': array([0.00653849, 0.00614457, 0.00613132, 0.00614538, 0.00614262,\n",
              "         0.00677257, 0.00668726, 0.00675406, 0.00672469, 0.00657935,\n",
              "         0.00738587, 0.00716372, 0.0071681 , 0.00734129, 0.006917  ,\n",
              "         0.00756946, 0.00752988, 0.00749302, 0.00734549, 0.00724516,\n",
              "         0.00789227, 0.00772715, 0.00758004, 0.00755405, 0.00721326]),\n",
              "  'mean_test_score': array([0.84350596, 0.84539506, 0.84513634, 0.84523376, 0.84500423,\n",
              "         0.84303827, 0.84317968, 0.84321582, 0.84356301, 0.84444605,\n",
              "         0.84189866, 0.84086484, 0.84214285, 0.84234019, 0.84326609,\n",
              "         0.83943562, 0.84043105, 0.84044683, 0.84067936, 0.84160174,\n",
              "         0.83793307, 0.83905003, 0.83998942, 0.8404559 , 0.84161988]),\n",
              "  'mean_train_score': array([0.88767187, 0.88560265, 0.88406431, 0.8823102 , 0.88154261,\n",
              "         0.90002083, 0.8966265 , 0.89396688, 0.89145018, 0.88902145,\n",
              "         0.91041047, 0.90512838, 0.90149971, 0.89814357, 0.89440826,\n",
              "         0.91758409, 0.91176831, 0.90613064, 0.90168702, 0.89814501,\n",
              "         0.92202018, 0.91590616, 0.90966859, 0.90400883, 0.89984424]),\n",
              "  'param_max_depth': masked_array(data=[5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8,\n",
              "                     8, 8, 9, 9, 9, 9, 9],\n",
              "               mask=[False, False, False, False, False, False, False, False,\n",
              "                     False, False, False, False, False, False, False, False,\n",
              "                     False, False, False, False, False, False, False, False,\n",
              "                     False],\n",
              "         fill_value='?',\n",
              "              dtype=object),\n",
              "  'param_min_child_weight': masked_array(data=[5, 6, 7, 8, 9, 5, 6, 7, 8, 9, 5, 6, 7, 8, 9, 5, 6, 7,\n",
              "                     8, 9, 5, 6, 7, 8, 9],\n",
              "               mask=[False, False, False, False, False, False, False, False,\n",
              "                     False, False, False, False, False, False, False, False,\n",
              "                     False, False, False, False, False, False, False, False,\n",
              "                     False],\n",
              "         fill_value='?',\n",
              "              dtype=object),\n",
              "  'params': [{'max_depth': 5, 'min_child_weight': 5},\n",
              "   {'max_depth': 5, 'min_child_weight': 6},\n",
              "   {'max_depth': 5, 'min_child_weight': 7},\n",
              "   {'max_depth': 5, 'min_child_weight': 8},\n",
              "   {'max_depth': 5, 'min_child_weight': 9},\n",
              "   {'max_depth': 6, 'min_child_weight': 5},\n",
              "   {'max_depth': 6, 'min_child_weight': 6},\n",
              "   {'max_depth': 6, 'min_child_weight': 7},\n",
              "   {'max_depth': 6, 'min_child_weight': 8},\n",
              "   {'max_depth': 6, 'min_child_weight': 9},\n",
              "   {'max_depth': 7, 'min_child_weight': 5},\n",
              "   {'max_depth': 7, 'min_child_weight': 6},\n",
              "   {'max_depth': 7, 'min_child_weight': 7},\n",
              "   {'max_depth': 7, 'min_child_weight': 8},\n",
              "   {'max_depth': 7, 'min_child_weight': 9},\n",
              "   {'max_depth': 8, 'min_child_weight': 5},\n",
              "   {'max_depth': 8, 'min_child_weight': 6},\n",
              "   {'max_depth': 8, 'min_child_weight': 7},\n",
              "   {'max_depth': 8, 'min_child_weight': 8},\n",
              "   {'max_depth': 8, 'min_child_weight': 9},\n",
              "   {'max_depth': 9, 'min_child_weight': 5},\n",
              "   {'max_depth': 9, 'min_child_weight': 6},\n",
              "   {'max_depth': 9, 'min_child_weight': 7},\n",
              "   {'max_depth': 9, 'min_child_weight': 8},\n",
              "   {'max_depth': 9, 'min_child_weight': 9}],\n",
              "  'rank_test_score': array([ 7,  1,  3,  2,  4, 11, 10,  9,  6,  5, 14, 17, 13, 12,  8, 23, 21,\n",
              "         20, 18, 16, 25, 24, 22, 19, 15], dtype=int32),\n",
              "  'split0_test_score': array([0.85533496, 0.85848295, 0.85964135, 0.85946191, 0.85960968,\n",
              "         0.85605797, 0.85637461, 0.85443252, 0.85820852, 0.8591954 ,\n",
              "         0.85445363, 0.85355646, 0.85305247, 0.85441405, 0.85572285,\n",
              "         0.85335328, 0.85304191, 0.85166978, 0.8531026 , 0.85502095,\n",
              "         0.85157215, 0.85103913, 0.85164867, 0.85140855, 0.85455126]),\n",
              "  'split0_train_score': array([0.88630849, 0.88413238, 0.88187119, 0.88028313, 0.87891934,\n",
              "         0.89834564, 0.89356391, 0.8907736 , 0.88818437, 0.88543047,\n",
              "         0.90849134, 0.90241369, 0.8988751 , 0.89501791, 0.89122246,\n",
              "         0.91541029, 0.90944683, 0.90365997, 0.89851959, 0.89479034,\n",
              "         0.9209901 , 0.91266714, 0.90661894, 0.90047226, 0.8966495 ]),\n",
              "  'split1_test_score': array([0.86176815, 0.86208216, 0.86366803, 0.86432771, 0.86315612,\n",
              "         0.86230117, 0.86139345, 0.86055698, 0.86137234, 0.86060712,\n",
              "         0.86025353, 0.85831671, 0.86078127, 0.86040921, 0.86164677,\n",
              "         0.85672292, 0.8595358 , 0.8587389 , 0.85800006, 0.85690763,\n",
              "         0.85471222, 0.85673348, 0.85422142, 0.85846976, 0.86049101]),\n",
              "  'split1_train_score': array([0.88363999, 0.8811072 , 0.87970882, 0.87791801, 0.87807193,\n",
              "         0.89722167, 0.89424465, 0.89182259, 0.88859285, 0.88638976,\n",
              "         0.90832352, 0.90377384, 0.89964091, 0.89565479, 0.89128734,\n",
              "         0.91559317, 0.9094753 , 0.90397096, 0.90019255, 0.89575178,\n",
              "         0.9197127 , 0.91436013, 0.90821743, 0.90235079, 0.89687294]),\n",
              "  'split2_test_score': array([0.81757523, 0.81990623, 0.81852617, 0.81932613, 0.81871954,\n",
              "         0.81587466, 0.81703751, 0.81771297, 0.81484425, 0.81874073,\n",
              "         0.81657396, 0.81532634, 0.81548262, 0.81560712, 0.81684414,\n",
              "         0.81267218, 0.81395953, 0.81499258, 0.81561772, 0.81693155,\n",
              "         0.81224306, 0.81339532, 0.81723088, 0.81488928, 0.81627993]),\n",
              "  'split2_train_score': array([0.89099069, 0.88938084, 0.88906701, 0.88680105, 0.88642967,\n",
              "         0.90339114, 0.90015655, 0.89843344, 0.89634821, 0.89388498,\n",
              "         0.91316102, 0.90807683, 0.90516038, 0.90238299, 0.89950127,\n",
              "         0.92076019, 0.91463495, 0.91007013, 0.90584427, 0.9031365 ,\n",
              "         0.92455613, 0.91846364, 0.91328272, 0.90766709, 0.90365255]),\n",
              "  'split3_test_score': array([0.86040584, 0.86242175, 0.86137666, 0.86018568, 0.86159151,\n",
              "         0.85923342, 0.85971353, 0.85983024, 0.86107958, 0.86200796,\n",
              "         0.8575252 , 0.85744562, 0.85940584, 0.8597878 , 0.85980902,\n",
              "         0.85613793, 0.85676127, 0.85572679, 0.85442706, 0.85940053,\n",
              "         0.85435279, 0.8555305 , 0.855     , 0.85561008, 0.85731565]),\n",
              "  'split3_train_score': array([0.88514281, 0.88368691, 0.88124503, 0.87954003, 0.87928201,\n",
              "         0.89676376, 0.89369161, 0.89063336, 0.88962639, 0.8864316 ,\n",
              "         0.90833329, 0.90282704, 0.89891356, 0.89600985, 0.89193951,\n",
              "         0.91557314, 0.90950754, 0.90393765, 0.89954201, 0.89668441,\n",
              "         0.9202997 , 0.9147313 , 0.90729608, 0.9023872 , 0.89886943]),\n",
              "  'split4_test_score': array([0.82244562, 0.82408223, 0.8224695 , 0.82286737, 0.8219443 ,\n",
              "         0.82172414, 0.82137931, 0.82354642, 0.82231034, 0.82167905,\n",
              "         0.820687  , 0.81967905, 0.82199204, 0.82148276, 0.82230769,\n",
              "         0.81829178, 0.81885676, 0.8211061 , 0.82224934, 0.81974801,\n",
              "         0.81678515, 0.81855172, 0.82184615, 0.82190186, 0.81946154]),\n",
              "  'split4_train_score': array([0.89227736, 0.8897059 , 0.8884295 , 0.88700881, 0.88501008,\n",
              "         0.90438196, 0.90147577, 0.89817139, 0.89449907, 0.89297044,\n",
              "         0.91374318, 0.90855049, 0.90490858, 0.9016523 , 0.89809073,\n",
              "         0.92058368, 0.91577695, 0.90901447, 0.90433667, 0.90036203,\n",
              "         0.92454229, 0.9193086 , 0.91292779, 0.90716682, 0.9031768 ]),\n",
              "  'std_fit_time': array([0.0006232 , 0.00222392, 0.00071557, 0.00141248, 0.00287078,\n",
              "         0.00302355, 0.00421593, 0.00283339, 0.00067   , 0.00075281,\n",
              "         0.00040904, 0.0030883 , 0.00151462, 0.00315502, 0.00373123,\n",
              "         0.00543478, 0.00418169, 0.00224869, 0.00386291, 0.00296154,\n",
              "         0.0026668 , 0.002147  , 0.00540497, 0.00303361, 0.01307323]),\n",
              "  'std_score_time': array([5.17995880e-04, 4.87567801e-05, 6.88963857e-05, 9.80356324e-05,\n",
              "         1.15736247e-04, 1.29857073e-04, 9.10139123e-05, 1.42581216e-04,\n",
              "         1.43927402e-04, 6.40586666e-05, 3.89769610e-04, 7.13413409e-05,\n",
              "         9.65980053e-05, 5.43650168e-04, 4.99744906e-05, 1.01841143e-04,\n",
              "         8.67198348e-05, 1.02682871e-04, 8.94820724e-05, 1.22000686e-04,\n",
              "         1.31548613e-04, 8.99962397e-05, 1.82735049e-05, 6.34969151e-05,\n",
              "         7.68520899e-04]),\n",
              "  'std_test_score': array([0.01936481, 0.01920197, 0.0201963 , 0.01980929, 0.02020195,\n",
              "         0.01997503, 0.01968698, 0.01865395, 0.02056666, 0.0198305 ,\n",
              "         0.01913109, 0.01919177, 0.01939713, 0.01962843, 0.01951442,\n",
              "         0.01967162, 0.01978329, 0.01852573, 0.01795046, 0.01906487,\n",
              "         0.01920616, 0.01900733, 0.01679842, 0.01828668, 0.01950796]),\n",
              "  'std_train_score': array([0.00336857, 0.00338089, 0.00389385, 0.00382933, 0.00346249,\n",
              "         0.00321336, 0.00345378, 0.0035647 , 0.00332994, 0.00362703,\n",
              "         0.00249101, 0.00264215, 0.0029001 , 0.00318749, 0.00361894,\n",
              "         0.00252263, 0.00283002, 0.00280762, 0.00286951, 0.00312873,\n",
              "         0.00210417, 0.00254476, 0.00285375, 0.002872  , 0.00301975])},\n",
              " {'max_depth': 5, 'min_child_weight': 6},\n",
              " 0.8453950633251575)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vc7mUGry0r6"
      },
      "source": [
        "# If the best parameters are edge values then we do \n",
        "# GridSearchCV by taking one less and one value more than the best parameters."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ToEbGG2y0sF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1779
        },
        "outputId": "390d1fba-4859-4fea-8ac6-96cb10774fab"
      },
      "source": [
        "param_test2 = {\n",
        " 'max_depth':[6,7,8,9],\n",
        " 'min_child_weight':[2,3,4,5]\n",
        "}\n",
        "gsearch2 = GridSearchCV(estimator=XgbClass(n_estimators=48),param_grid =param_test2,scoring='roc_auc',n_jobs=-1,iid=False, cv=5)\n",
        "gsearch2.fit(train[predictors],train[target])\n",
        "gsearch2.cv_results_, gsearch2.best_params_, gsearch2.best_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'mean_fit_time': array([0.25651345, 0.24808664, 0.25345564, 0.24944706, 0.28951616,\n",
              "         0.28822298, 0.28571844, 0.28517208, 0.32676215, 0.32411208,\n",
              "         0.32022843, 0.31513767, 0.36551666, 0.35565701, 0.34868417,\n",
              "         0.3419539 ]),\n",
              "  'mean_score_time': array([0.00697908, 0.00703349, 0.00803852, 0.00803237, 0.00733232,\n",
              "         0.00735245, 0.00715566, 0.00723968, 0.00774646, 0.00772562,\n",
              "         0.00789828, 0.00892334, 0.00814891, 0.00829682, 0.0079906 ,\n",
              "         0.00785379]),\n",
              "  'mean_test_score': array([0.84042715, 0.84189787, 0.8428498 , 0.84303827, 0.8398022 ,\n",
              "         0.83976764, 0.84035653, 0.84189866, 0.83624953, 0.8369503 ,\n",
              "         0.83858558, 0.83943562, 0.83412339, 0.83550116, 0.83753711,\n",
              "         0.83793307]),\n",
              "  'mean_train_score': array([0.91253411, 0.90805523, 0.90370692, 0.90002083, 0.93031609,\n",
              "         0.92219569, 0.9160066 , 0.91041047, 0.94335466, 0.93313675,\n",
              "         0.92526439, 0.91758409, 0.95263699, 0.94049106, 0.93067881,\n",
              "         0.92202018]),\n",
              "  'param_max_depth': masked_array(data=[6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9],\n",
              "               mask=[False, False, False, False, False, False, False, False,\n",
              "                     False, False, False, False, False, False, False, False],\n",
              "         fill_value='?',\n",
              "              dtype=object),\n",
              "  'param_min_child_weight': masked_array(data=[2, 3, 4, 5, 2, 3, 4, 5, 2, 3, 4, 5, 2, 3, 4, 5],\n",
              "               mask=[False, False, False, False, False, False, False, False,\n",
              "                     False, False, False, False, False, False, False, False],\n",
              "         fill_value='?',\n",
              "              dtype=object),\n",
              "  'params': [{'max_depth': 6, 'min_child_weight': 2},\n",
              "   {'max_depth': 6, 'min_child_weight': 3},\n",
              "   {'max_depth': 6, 'min_child_weight': 4},\n",
              "   {'max_depth': 6, 'min_child_weight': 5},\n",
              "   {'max_depth': 7, 'min_child_weight': 2},\n",
              "   {'max_depth': 7, 'min_child_weight': 3},\n",
              "   {'max_depth': 7, 'min_child_weight': 4},\n",
              "   {'max_depth': 7, 'min_child_weight': 5},\n",
              "   {'max_depth': 8, 'min_child_weight': 2},\n",
              "   {'max_depth': 8, 'min_child_weight': 3},\n",
              "   {'max_depth': 8, 'min_child_weight': 4},\n",
              "   {'max_depth': 8, 'min_child_weight': 5},\n",
              "   {'max_depth': 9, 'min_child_weight': 2},\n",
              "   {'max_depth': 9, 'min_child_weight': 3},\n",
              "   {'max_depth': 9, 'min_child_weight': 4},\n",
              "   {'max_depth': 9, 'min_child_weight': 5}],\n",
              "  'rank_test_score': array([ 5,  4,  2,  1,  7,  8,  6,  3, 14, 13, 10,  9, 16, 15, 12, 11],\n",
              "        dtype=int32),\n",
              "  'split0_test_score': array([0.85201281, 0.85469639, 0.85603686, 0.85605797, 0.85242973,\n",
              "         0.85254847, 0.85429003, 0.85445363, 0.85053777, 0.85385728,\n",
              "         0.85487054, 0.85335328, 0.84824736, 0.84973032, 0.85418976,\n",
              "         0.85157215]),\n",
              "  'split0_train_score': array([0.91148358, 0.90560685, 0.90161312, 0.89834564, 0.92849395,\n",
              "         0.92024249, 0.91378102, 0.90849134, 0.94190912, 0.93158301,\n",
              "         0.92334943, 0.91541029, 0.9510391 , 0.93836176, 0.92880974,\n",
              "         0.9209901 ]),\n",
              "  'split1_test_score': array([0.85990522, 0.85973634, 0.86111375, 0.86230117, 0.85988938,\n",
              "         0.8550447 , 0.85755148, 0.86025353, 0.85225294, 0.8535723 ,\n",
              "         0.85539829, 0.85672292, 0.8508122 , 0.85301289, 0.85464889,\n",
              "         0.85471222]),\n",
              "  'split1_train_score': array([0.90947331, 0.90446385, 0.90123625, 0.89722167, 0.92682446,\n",
              "         0.9191551 , 0.91332802, 0.90832352, 0.94059167, 0.93181902,\n",
              "         0.92325393, 0.91559317, 0.95017398, 0.93944883, 0.92811973,\n",
              "         0.9197127 ]),\n",
              "  'split2_test_score': array([0.8133185 , 0.81663488, 0.81519919, 0.81587466, 0.81185103,\n",
              "         0.81467472, 0.81574751, 0.81657396, 0.80718902, 0.80890284,\n",
              "         0.81256357, 0.81267218, 0.80743272, 0.80867504, 0.80908296,\n",
              "         0.81224306]),\n",
              "  'split2_train_score': array([0.91551908, 0.91167832, 0.90627402, 0.90339114, 0.93302008,\n",
              "         0.92473686, 0.91910834, 0.91316102, 0.94496118, 0.93506216,\n",
              "         0.92860407, 0.92076019, 0.9545257 , 0.94268249, 0.93376879,\n",
              "         0.92455613]),\n",
              "  'split3_test_score': array([0.85745623, 0.85734218, 0.85870822, 0.85923342, 0.85657029,\n",
              "         0.85755172, 0.85538727, 0.8575252 , 0.85302653, 0.85191777,\n",
              "         0.85208753, 0.85613793, 0.85095491, 0.85195491, 0.85145889,\n",
              "         0.85435279]),\n",
              "  'split3_train_score': array([0.91158164, 0.90663953, 0.9014004 , 0.89676376, 0.92998374,\n",
              "         0.92220603, 0.91476188, 0.90833329, 0.94454013, 0.93234   ,\n",
              "         0.92414641, 0.91557314, 0.95348696, 0.93986018, 0.92970754,\n",
              "         0.9202997 ]),\n",
              "  'split4_test_score': array([0.81944297, 0.82107958, 0.82319098, 0.82172414, 0.81827056,\n",
              "         0.81901857, 0.81880637, 0.820687  , 0.81824138, 0.81650133,\n",
              "         0.81800796, 0.81829178, 0.81316976, 0.81413263, 0.81830504,\n",
              "         0.81678515]),\n",
              "  'split4_train_score': array([0.91461295, 0.9118876 , 0.90801081, 0.90438196, 0.9332582 ,\n",
              "         0.92463799, 0.91905372, 0.91374318, 0.94477121, 0.93487956,\n",
              "         0.92696813, 0.92058368, 0.9539592 , 0.94210205, 0.93298828,\n",
              "         0.92454229]),\n",
              "  'std_fit_time': array([0.00494181, 0.00113587, 0.00906541, 0.00404031, 0.00261352,\n",
              "         0.00370727, 0.0011786 , 0.00209242, 0.00205462, 0.00197183,\n",
              "         0.00304541, 0.00198408, 0.00309444, 0.00160216, 0.00476515,\n",
              "         0.00210007]),\n",
              "  'std_score_time': array([2.21049518e-04, 5.95517947e-04, 2.34343434e-03, 2.20813946e-03,\n",
              "         1.06994969e-04, 1.54404231e-04, 1.23558528e-04, 1.59221032e-04,\n",
              "         9.51900024e-05, 8.68762232e-05, 2.98474710e-04, 2.33817485e-03,\n",
              "         8.16554918e-05, 2.18232568e-04, 1.15529289e-04, 9.60403070e-05]),\n",
              "  'std_test_score': array([0.01989385, 0.0189323 , 0.01954474, 0.01997503, 0.02044015,\n",
              "         0.01883184, 0.01889839, 0.01913109, 0.01954757, 0.01995486,\n",
              "         0.01913506, 0.01967162, 0.01955888, 0.01977934, 0.01971523,\n",
              "         0.01920616]),\n",
              "  'std_train_score': array([0.0022186 , 0.00312124, 0.00286083, 0.00321336, 0.00251355,\n",
              "         0.00225755, 0.00255277, 0.00249101, 0.00177294, 0.00151855,\n",
              "         0.00214547, 0.00252263, 0.00171217, 0.00163804, 0.00227452,\n",
              "         0.00210417])},\n",
              " {'max_depth': 6, 'min_child_weight': 5},\n",
              " 0.8430382710636628)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBhirbgNy0sK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "e946d1cf-6785-4261-9680-1d57e9130382"
      },
      "source": [
        "xgb1 = XgbClass(max_depth=8,min_child_weight=4)\n",
        "model=modelfit(xgb1, train, predictors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'base_score': 0.5, 'booster': 'gbtree', 'colsample_bylevel': 1, 'colsample_bytree': 0.8, 'gamma': 0, 'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 8, 'min_child_weight': 4, 'missing': None, 'n_estimators': 20, 'nthread': 1, 'objective': 'binary:logistic', 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1, 'seed': 0, 'silent': 1, 'subsample': 0.8}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AHAD8Lhy0sM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "57beb19f-2f49-4280-dc7b-f9c37eea34cf"
      },
      "source": [
        "get_accuracy(model,predictors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model Report\n",
            "Accuracy : 0.7979\n",
            "AUC Score (Train): 0.843505\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U6Yk0ZKWy0sO"
      },
      "source": [
        "# To tune gamma."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15q5_HFVy0sT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 898
        },
        "outputId": "49c77d37-ac8c-49dd-a6da-e7285d0e36f8"
      },
      "source": [
        "param_test3 = {\n",
        " 'gamma':[i/10.0 for i in range(0,8)]\n",
        "}\n",
        "gsearch3=GridSearchCV(estimator=XgbClass(n_estimators=48,max_depth=7,min_child_weight=5),\n",
        "                      param_grid =param_test3,scoring='roc_auc',n_jobs=-1,iid=False, cv=5)\n",
        "gsearch3.fit(train[predictors],train[target])\n",
        "gsearch3.cv_results_, gsearch3.best_params_, gsearch3.best_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'mean_fit_time': array([0.28967605, 0.28326716, 0.28377404, 0.28291426, 0.28900743,\n",
              "         0.29015837, 0.29366841, 0.28920174]),\n",
              "  'mean_score_time': array([0.00724435, 0.00734229, 0.00721374, 0.0086863 , 0.00774436,\n",
              "         0.007374  , 0.00738888, 0.00745301]),\n",
              "  'mean_test_score': array([0.84189866, 0.84089109, 0.84198526, 0.84263784, 0.8409183 ,\n",
              "         0.8416148 , 0.84014798, 0.84140207]),\n",
              "  'mean_train_score': array([0.91041047, 0.91040467, 0.91002945, 0.90957837, 0.90951727,\n",
              "         0.90899937, 0.90847181, 0.9084777 ]),\n",
              "  'param_gamma': masked_array(data=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7],\n",
              "               mask=[False, False, False, False, False, False, False, False],\n",
              "         fill_value='?',\n",
              "              dtype=object),\n",
              "  'params': [{'gamma': 0.0},\n",
              "   {'gamma': 0.1},\n",
              "   {'gamma': 0.2},\n",
              "   {'gamma': 0.3},\n",
              "   {'gamma': 0.4},\n",
              "   {'gamma': 0.5},\n",
              "   {'gamma': 0.6},\n",
              "   {'gamma': 0.7}],\n",
              "  'rank_test_score': array([3, 7, 2, 1, 6, 4, 8, 5], dtype=int32),\n",
              "  'split0_test_score': array([0.85445363, 0.85309733, 0.85385728, 0.85648544, 0.85578354,\n",
              "         0.85621365, 0.85276749, 0.85435072]),\n",
              "  'split0_train_score': array([0.90849134, 0.90862491, 0.9075261 , 0.90689203, 0.90746221,\n",
              "         0.90674291, 0.90655903, 0.90616247]),\n",
              "  'split1_test_score': array([0.86025353, 0.85877057, 0.86091321, 0.86234867, 0.85950413,\n",
              "         0.86038546, 0.85921387, 0.85956218]),\n",
              "  'split1_train_score': array([0.90832352, 0.90845526, 0.90805506, 0.90781805, 0.90647346,\n",
              "         0.90588077, 0.90692894, 0.90635926]),\n",
              "  'split2_test_score': array([0.81657396, 0.81441513, 0.8158879 , 0.81562036, 0.81240199,\n",
              "         0.81340061, 0.81352511, 0.81587466]),\n",
              "  'split2_train_score': array([0.91316102, 0.91337118, 0.9137538 , 0.91278303, 0.91264629,\n",
              "         0.91240107, 0.91079651, 0.91175009]),\n",
              "  'split3_test_score': array([0.8575252 , 0.85714324, 0.85908488, 0.85719629, 0.85713528,\n",
              "         0.8570557 , 0.85439788, 0.85738992]),\n",
              "  'split3_train_score': array([0.90833329, 0.90773328, 0.90699326, 0.90662218, 0.90710401,\n",
              "         0.90726269, 0.9053357 , 0.90596101]),\n",
              "  'split4_test_score': array([0.820687  , 0.82102918, 0.82018302, 0.82153846, 0.81976658,\n",
              "         0.82101857, 0.82083554, 0.81983289]),\n",
              "  'split4_train_score': array([0.91374318, 0.91383872, 0.91381905, 0.91377657, 0.91390037,\n",
              "         0.91270943, 0.91273886, 0.9121557 ]),\n",
              "  'std_fit_time': array([0.00835323, 0.00239522, 0.00252602, 0.00213567, 0.00506065,\n",
              "         0.00166652, 0.00445395, 0.00285593]),\n",
              "  'std_score_time': array([1.31115913e-04, 1.56521563e-04, 1.63058481e-04, 2.64334106e-03,\n",
              "         5.22730925e-04, 6.41514178e-05, 9.90320179e-05, 1.91955440e-04]),\n",
              "  'std_test_score': array([0.01913109, 0.01912209, 0.01973838, 0.01983607, 0.02044492,\n",
              "         0.02012029, 0.01901351, 0.01933881]),\n",
              "  'std_train_score': array([0.00249101, 0.00263427, 0.00308594, 0.00306428, 0.00310851,\n",
              "         0.00293835, 0.00281021, 0.00284317])},\n",
              " {'gamma': 0.3},\n",
              " 0.8426378443221705)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OU1BTcThy0sW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "3c0c75df-2391-4794-c182-75058f3be23d"
      },
      "source": [
        "xgb1 = XgbClass(max_depth=7,min_child_weight=5,gamma=0)\n",
        "model=modelfit(xgb1, train, predictors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'base_score': 0.5, 'booster': 'gbtree', 'colsample_bylevel': 1, 'colsample_bytree': 0.8, 'gamma': 0, 'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 7, 'min_child_weight': 5, 'missing': None, 'n_estimators': 20, 'nthread': 1, 'objective': 'binary:logistic', 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1, 'seed': 0, 'silent': 1, 'subsample': 0.8}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3gsJhBfAy0sb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "0f9cf70a-6e5e-47b7-e907-a17864ad9ea4"
      },
      "source": [
        "get_accuracy(model,predictors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model Report\n",
            "Accuracy : 0.8027\n",
            "AUC Score (Train): 0.844282\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cyFX2Zovy0sb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1814
        },
        "outputId": "eaca23db-94ce-4d00-b157-ef91ce72e586"
      },
      "source": [
        "param_test4 = {\n",
        " 'subsample':[i/10.0 for i in range(6,10)],\n",
        " 'colsample_bytree':[i/10.0 for i in range(6,10)]\n",
        "}\n",
        "gsearch4=GridSearchCV(estimator=XgbClass(n_estimators=48,max_depth=7,\n",
        "                                         min_child_weight=5,gamma=0),\n",
        "                      param_grid =param_test4,scoring='roc_auc',n_jobs=-1,iid=False, cv=5)\n",
        "gsearch4.fit(train[predictors],train[target])\n",
        "gsearch4.cv_results_, gsearch4.best_params_, gsearch4.best_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'mean_fit_time': array([0.24761925, 0.24028883, 0.23841305, 0.23658376, 0.27860475,\n",
              "         0.27579222, 0.27358937, 0.25988464, 0.28870115, 0.28766732,\n",
              "         0.28121572, 0.27407904, 0.304284  , 0.30221763, 0.29781356,\n",
              "         0.28894463]),\n",
              "  'mean_score_time': array([0.00745382, 0.00756383, 0.00910616, 0.00766773, 0.00748549,\n",
              "         0.00750766, 0.00747414, 0.00724821, 0.00714731, 0.00728402,\n",
              "         0.00721726, 0.00721107, 0.00716071, 0.00730948, 0.00833058,\n",
              "         0.00849657]),\n",
              "  'mean_test_score': array([0.84051166, 0.84041769, 0.84022394, 0.84168989, 0.83987525,\n",
              "         0.84008881, 0.84050073, 0.84057328, 0.84029717, 0.83995671,\n",
              "         0.84189866, 0.83927434, 0.83927259, 0.83936597, 0.84106022,\n",
              "         0.83889594]),\n",
              "  'mean_train_score': array([0.89651826, 0.90120841, 0.90492691, 0.90857003, 0.89975845,\n",
              "         0.90490393, 0.90868614, 0.91178254, 0.90200789, 0.90601781,\n",
              "         0.91041047, 0.91265981, 0.90269874, 0.90713974, 0.91118315,\n",
              "         0.91401333]),\n",
              "  'param_colsample_bytree': masked_array(data=[0.6, 0.6, 0.6, 0.6, 0.7, 0.7, 0.7, 0.7, 0.8, 0.8, 0.8,\n",
              "                     0.8, 0.9, 0.9, 0.9, 0.9],\n",
              "               mask=[False, False, False, False, False, False, False, False,\n",
              "                     False, False, False, False, False, False, False, False],\n",
              "         fill_value='?',\n",
              "              dtype=object),\n",
              "  'param_subsample': masked_array(data=[0.6, 0.7, 0.8, 0.9, 0.6, 0.7, 0.8, 0.9, 0.6, 0.7, 0.8,\n",
              "                     0.9, 0.6, 0.7, 0.8, 0.9],\n",
              "               mask=[False, False, False, False, False, False, False, False,\n",
              "                     False, False, False, False, False, False, False, False],\n",
              "         fill_value='?',\n",
              "              dtype=object),\n",
              "  'params': [{'colsample_bytree': 0.6, 'subsample': 0.6},\n",
              "   {'colsample_bytree': 0.6, 'subsample': 0.7},\n",
              "   {'colsample_bytree': 0.6, 'subsample': 0.8},\n",
              "   {'colsample_bytree': 0.6, 'subsample': 0.9},\n",
              "   {'colsample_bytree': 0.7, 'subsample': 0.6},\n",
              "   {'colsample_bytree': 0.7, 'subsample': 0.7},\n",
              "   {'colsample_bytree': 0.7, 'subsample': 0.8},\n",
              "   {'colsample_bytree': 0.7, 'subsample': 0.9},\n",
              "   {'colsample_bytree': 0.8, 'subsample': 0.6},\n",
              "   {'colsample_bytree': 0.8, 'subsample': 0.7},\n",
              "   {'colsample_bytree': 0.8, 'subsample': 0.8},\n",
              "   {'colsample_bytree': 0.8, 'subsample': 0.9},\n",
              "   {'colsample_bytree': 0.9, 'subsample': 0.6},\n",
              "   {'colsample_bytree': 0.9, 'subsample': 0.7},\n",
              "   {'colsample_bytree': 0.9, 'subsample': 0.8},\n",
              "   {'colsample_bytree': 0.9, 'subsample': 0.9}],\n",
              "  'rank_test_score': array([ 5,  7,  9,  2, 12, 10,  6,  4,  8, 11,  1, 14, 15, 13,  3, 16],\n",
              "        dtype=int32),\n",
              "  'split0_test_score': array([0.85614768, 0.8572665 , 0.85487846, 0.85740635, 0.85199434,\n",
              "         0.85353535, 0.8562374 , 0.85699471, 0.85311844, 0.85387575,\n",
              "         0.85445363, 0.85213683, 0.85002586, 0.85284665, 0.85399977,\n",
              "         0.85325037]),\n",
              "  'split0_train_score': array([0.8923226 , 0.89858712, 0.90090639, 0.90472254, 0.89619716,\n",
              "         0.90247145, 0.90562903, 0.90960406, 0.89723242, 0.90449033,\n",
              "         0.90849134, 0.91166862, 0.90043651, 0.90732004, 0.91078398,\n",
              "         0.91472707]),\n",
              "  'split1_test_score': array([0.85865447, 0.8587864 , 0.85941442, 0.85673348, 0.85955691,\n",
              "         0.85942497, 0.85737205, 0.85561466, 0.8600952 , 0.85860169,\n",
              "         0.86025353, 0.85567271, 0.85826921, 0.85884445, 0.85443252,\n",
              "         0.85375173]),\n",
              "  'split1_train_score': array([0.89336613, 0.89703298, 0.90146863, 0.90499497, 0.89617465,\n",
              "         0.90115251, 0.90611017, 0.9060764 , 0.89817169, 0.90205999,\n",
              "         0.90832352, 0.90871594, 0.89899725, 0.90462224, 0.91010556,\n",
              "         0.91059397]),\n",
              "  'split2_test_score': array([0.81333704, 0.81269866, 0.81208148, 0.81590644, 0.81340326,\n",
              "         0.81274634, 0.81131331, 0.81558858, 0.81418733, 0.8145926 ,\n",
              "         0.81657396, 0.8116709 , 0.8142509 , 0.8128523 , 0.81624815,\n",
              "         0.81336883]),\n",
              "  'split2_train_score': array([0.9008955 , 0.90581815, 0.90840224, 0.9126362 , 0.90556516,\n",
              "         0.90941418, 0.91373429, 0.9160229 , 0.90787428, 0.9104291 ,\n",
              "         0.91316102, 0.91546551, 0.90738716, 0.90897171, 0.91313986,\n",
              "         0.91593642]),\n",
              "  'split3_test_score': array([0.85693103, 0.85822812, 0.85761804, 0.85865517, 0.85524403,\n",
              "         0.85927586, 0.8574191 , 0.8552069 , 0.85477188, 0.85667374,\n",
              "         0.8575252 , 0.85645358, 0.85617772, 0.85636605, 0.8595252 ,\n",
              "         0.85740584]),\n",
              "  'split3_train_score': array([0.89500685, 0.89910613, 0.9033583 , 0.90722699, 0.89675053,\n",
              "         0.9030449 , 0.90599803, 0.91067319, 0.90041227, 0.90401534,\n",
              "         0.90833329, 0.91105716, 0.90023177, 0.9047861 , 0.90861578,\n",
              "         0.91064641]),\n",
              "  'split4_test_score': array([0.81748806, 0.81510875, 0.81712732, 0.81974801, 0.81917772,\n",
              "         0.81546154, 0.8201618 , 0.81946154, 0.819313  , 0.81603979,\n",
              "         0.820687  , 0.82043767, 0.81763926, 0.81592042, 0.82109549,\n",
              "         0.81670292]),\n",
              "  'split4_train_score': array([0.90100022, 0.90549769, 0.91049897, 0.91326945, 0.90410476,\n",
              "         0.9084366 , 0.91195917, 0.91653614, 0.90634878, 0.90909431,\n",
              "         0.91374318, 0.91639184, 0.90644102, 0.90999862, 0.91327061,\n",
              "         0.91816279]),\n",
              "  'std_fit_time': array([0.00486386, 0.00197052, 0.00177845, 0.00313342, 0.00133732,\n",
              "         0.00091447, 0.00168511, 0.00181217, 0.00133942, 0.00242639,\n",
              "         0.00169305, 0.00226738, 0.00303365, 0.00359041, 0.00268393,\n",
              "         0.00227884]),\n",
              "  'std_score_time': array([8.20058162e-05, 2.18495871e-04, 2.97623317e-03, 2.17010964e-04,\n",
              "         1.18402190e-04, 1.30700239e-04, 1.74339217e-04, 7.62777609e-05,\n",
              "         9.85414878e-05, 9.77379627e-05, 9.24649342e-05, 6.56049954e-05,\n",
              "         7.20679698e-05, 2.73254004e-04, 2.21648833e-03, 2.68110259e-03]),\n",
              "  'std_test_score': array([0.02055134, 0.02166744, 0.02102872, 0.01953136, 0.0194915 ,\n",
              "         0.02133988, 0.02041614, 0.01886791, 0.01943154, 0.02018014,\n",
              "         0.01913109, 0.01921586, 0.01926849, 0.02050761, 0.01844687,\n",
              "         0.01956284]),\n",
              "  'std_train_score': array([0.00371678, 0.00369791, 0.00383955, 0.00368799, 0.0041757 ,\n",
              "         0.00335465, 0.00344685, 0.00397778, 0.00432031, 0.00319161,\n",
              "         0.00249101, 0.00286025, 0.00348971, 0.00216514, 0.00179433,\n",
              "         0.00298174])},\n",
              " {'colsample_bytree': 0.8, 'subsample': 0.8},\n",
              " 0.8418986626168443)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMPJK63Fy0sf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "81f0e7fc-895a-4419-c30b-c5db9d6e81e5"
      },
      "source": [
        "xgb1 = XgbClass(max_depth=8,min_child_weight=4,gamma=0.4,subsample=0.8,colsample_bytree=0.6)\n",
        "model=modelfit(xgb1, train, predictors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'base_score': 0.5, 'booster': 'gbtree', 'colsample_bylevel': 1, 'colsample_bytree': 0.6, 'gamma': 0.4, 'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 8, 'min_child_weight': 4, 'missing': None, 'n_estimators': 18, 'nthread': 1, 'objective': 'binary:l