notebooks/XGBoost_on_Telco_customer_churn.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Introduction"
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, we will use **XGBoost** to build a collection of boosted trees, and use continuous and categorical data from [**IBM Base Samples**](https://www.ibm.com/docs/en/cognos-analytics/11.1.0?topic=samples-base) to predict whether or not a customer will stop using a company's service."
      ],
      "metadata": {
        "id": "kkOKS3_7azCU"
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    {
      "cell_type": "markdown",
      "source": [
        "# Necessary imports"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import xgboost as xgb\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import balanced_accuracy_score, roc_auc_score, make_scorer\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.compose import make_column_selector, make_column_transformer\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "doCYXeEWUFRd"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import the data"
      ],
      "metadata": {
        "id": "l1-r9IbTvLtA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we load in a dataset from the [**IBM Base Samples**](https://www.ibm.com/docs/en/cognos-analytics/11.1.0?topic=samples-base). Specifically, we are going to use the [**Telco Churn Dataset**](https://github.com/TrungKhoaLe/ds-ml-dl-collection/blob/main/dataset/telco_customer_churn.csv). This dataset will allow us to predict if someone will stop using Telco's services or not using a variety of continuous and categorical features."
      ],
      "metadata": {
        "id": "yd_zgqRU61xK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/TrungKhoaLe/ds-ml-dl-collection/main/dataset/telco_customer_churn.csv\")"
      ],
      "metadata": {
        "id": "EYzZZvthvIdS"
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      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's glance the first five samples of the dataset by using the ```head()``` function of the Pandas dataframe."
      ],
      "metadata": {
        "id": "_GE5yRJH8jl9"
      }
    },
    {
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      "source": [
        "df.head()"
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              "   CustomerID  Count        Country       State         City  Zip Code  \\\n",
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              "                 Lat Long   Latitude   Longitude  Gender  ...        Contract  \\\n",
              "0  33.964131, -118.272783  33.964131 -118.272783    Male  ...  Month-to-month   \n",
              "1   34.059281, -118.30742  34.059281 -118.307420  Female  ...  Month-to-month   \n",
              "2  34.048013, -118.293953  34.048013 -118.293953  Female  ...  Month-to-month   \n",
              "3  34.062125, -118.315709  34.062125 -118.315709  Female  ...  Month-to-month   \n",
              "4  34.039224, -118.266293  34.039224 -118.266293    Male  ...  Month-to-month   \n",
              "\n",
              "  Paperless Billing             Payment Method  Monthly Charges Total Charges  \\\n",
              "0               Yes               Mailed check            53.85        108.15   \n",
              "1               Yes           Electronic check            70.70        151.65   \n",
              "2               Yes           Electronic check            99.65         820.5   \n",
              "3               Yes           Electronic check           104.80       3046.05   \n",
              "4               Yes  Bank transfer (automatic)           103.70        5036.3   \n",
              "\n",
              "  Churn Label Churn Value Churn Score  CLTV                   Churn Reason  \n",
              "0         Yes           1          86  3239   Competitor made better offer  \n",
              "1         Yes           1          67  2701                          Moved  \n",
              "2         Yes           1          86  5372                          Moved  \n",
              "3         Yes           1          84  5003                          Moved  \n",
              "4         Yes           1          89  5340  Competitor had better devices  \n",
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      "source": [
        "The last four columns are sort of exit interview information that is collected from people that left Telco. \n",
        "\n",
        "We do not want to use this information in the prediction because someone is not going to do the exit interview before they leave the company.\n",
        "\n",
        "These features give us perfect predictive signals, so we are going to remove them from the dataset."
      ],
      "metadata": {
        "id": "efhKZ7jE8YqB"
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    {
      "cell_type": "code",
      "source": [
        "df.drop([\"Churn Label\", \"Churn Score\", \"CLTV\", \"Churn Reason\"],\n",
        "        axis=1, inplace=True)\n",
        "df.head()"
      ],
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      "execution_count": 4,
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              "   CustomerID  Count        Country       State         City  Zip Code  \\\n",
              "0  3668-QPYBK      1  United States  California  Los Angeles     90003   \n",
              "1  9237-HQITU      1  United States  California  Los Angeles     90005   \n",
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              "4  0280-XJGEX      1  United States  California  Los Angeles     90015   \n",
              "\n",
              "                 Lat Long   Latitude   Longitude  Gender  ...  \\\n",
              "0  33.964131, -118.272783  33.964131 -118.272783    Male  ...   \n",
              "1   34.059281, -118.30742  34.059281 -118.307420  Female  ...   \n",
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              "4  34.039224, -118.266293  34.039224 -118.266293    Male  ...   \n",
              "\n",
              "  Device Protection Tech Support Streaming TV  Streaming Movies  \\\n",
              "0                No           No           No                No   \n",
              "1                No           No           No                No   \n",
              "2               Yes           No          Yes               Yes   \n",
              "3               Yes          Yes          Yes               Yes   \n",
              "4               Yes           No          Yes               Yes   \n",
              "\n",
              "         Contract Paperless Billing             Payment Method  \\\n",
              "0  Month-to-month               Yes               Mailed check   \n",
              "1  Month-to-month               Yes           Electronic check   \n",
              "2  Month-to-month               Yes           Electronic check   \n",
              "3  Month-to-month               Yes           Electronic check   \n",
              "4  Month-to-month               Yes  Bank transfer (automatic)   \n",
              "\n",
              "  Monthly Charges Total Charges Churn Value  \n",
              "0           53.85        108.15           1  \n",
              "1           70.70        151.65           1  \n",
              "2           99.65         820.5           1  \n",
              "3          104.80       3046.05           1  \n",
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              "      <td>33.964131, -118.272783</td>\n",
              "      <td>33.964131</td>\n",
              "      <td>-118.272783</td>\n",
              "      <td>Male</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9237-HQITU</td>\n",
              "      <td>1</td>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>90005</td>\n",
              "      <td>34.059281, -118.30742</td>\n",
              "      <td>34.059281</td>\n",
              "      <td>-118.307420</td>\n",
              "      <td>Female</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9305-CDSKC</td>\n",
              "      <td>1</td>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>90006</td>\n",
              "      <td>34.048013, -118.293953</td>\n",
              "      <td>34.048013</td>\n",
              "      <td>-118.293953</td>\n",
              "      <td>Female</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>99.65</td>\n",
              "      <td>820.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7892-POOKP</td>\n",
              "      <td>1</td>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>90010</td>\n",
              "      <td>34.062125, -118.315709</td>\n",
              "      <td>34.062125</td>\n",
              "      <td>-118.315709</td>\n",
              "      <td>Female</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>104.80</td>\n",
              "      <td>3046.05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0280-XJGEX</td>\n",
              "      <td>1</td>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>90015</td>\n",
              "      <td>34.039224, -118.266293</td>\n",
              "      <td>34.039224</td>\n",
              "      <td>-118.266293</td>\n",
              "      <td>Male</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>103.70</td>\n",
              "      <td>5036.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 29 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9867dc31-d17a-46cc-9e99-40fead49f4de')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "                                                     [key], {});\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some of the other features only contain a single value, and will not be useful for classification. For example: "
      ],
      "metadata": {
        "id": "fTK1bQ2g_ob5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Count\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zjBXXgIc6q2N",
        "outputId": "e05b466c-3a64-4176-897e-44445a055b37"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Country\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vWWnc4Pl_5G_",
        "outputId": "f7d9ed85-0d5e-4821-e2cc-74109bedc343"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['United States'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"State\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QcU4W8Cg_9F6",
        "outputId": "ae2e8f36-8a99-4b9c-e897-6eb50fb64665"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['California'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can omit those features from the analysis because they are not going to help with predictions. In contrast, ```City``` contains a bunch of different city names, so we will leave it in."
      ],
      "metadata": {
        "id": "SbEM_70qAF15"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"City\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "phLXEBkFACBs",
        "outputId": "d60bb57c-8310-4cd3-9ec1-11810c969ab4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Los Angeles', 'Beverly Hills', 'Huntington Park', ..., 'Standish',\n",
              "       'Tulelake', 'Olympic Valley'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will also remove ```CustomerID``` because it is different for every customer and useless for classification. Lastly, we will drop ```Lat Long``` because thre are separate columns for ```Latitude``` and ```Longitude```."
      ],
      "metadata": {
        "id": "hrOUT8faBTJB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop([\"CustomerID\", \"Count\", \"Country\", \"State\", \"Lat Long\"],\n",
        "        axis=1, inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "ay33hWrZAu3c",
        "outputId": "b9bd5914-a2d3-4890-dc1a-417f8313f944"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          City  Zip Code   Latitude   Longitude  Gender Senior Citizen  \\\n",
              "0  Los Angeles     90003  33.964131 -118.272783    Male             No   \n",
              "1  Los Angeles     90005  34.059281 -118.307420  Female             No   \n",
              "2  Los Angeles     90006  34.048013 -118.293953  Female             No   \n",
              "3  Los Angeles     90010  34.062125 -118.315709  Female             No   \n",
              "4  Los Angeles     90015  34.039224 -118.266293    Male             No   \n",
              "\n",
              "  Partner Dependents  Tenure Months Phone Service  ... Device Protection  \\\n",
              "0      No         No              2           Yes  ...                No   \n",
              "1      No        Yes              2           Yes  ...                No   \n",
              "2      No        Yes              8           Yes  ...               Yes   \n",
              "3     Yes        Yes             28           Yes  ...               Yes   \n",
              "4      No        Yes             49           Yes  ...               Yes   \n",
              "\n",
              "  Tech Support Streaming TV Streaming Movies        Contract  \\\n",
              "0           No           No               No  Month-to-month   \n",
              "1           No           No               No  Month-to-month   \n",
              "2           No          Yes              Yes  Month-to-month   \n",
              "3          Yes          Yes              Yes  Month-to-month   \n",
              "4           No          Yes              Yes  Month-to-month   \n",
              "\n",
              "  Paperless Billing             Payment Method Monthly Charges Total Charges  \\\n",
              "0               Yes               Mailed check           53.85        108.15   \n",
              "1               Yes           Electronic check           70.70        151.65   \n",
              "2               Yes           Electronic check           99.65         820.5   \n",
              "3               Yes           Electronic check          104.80       3046.05   \n",
              "4               Yes  Bank transfer (automatic)          103.70        5036.3   \n",
              "\n",
              "  Churn Value  \n",
              "0           1  \n",
              "1           1  \n",
              "2           1  \n",
              "3           1  \n",
              "4           1  \n",
              "\n",
              "[5 rows x 24 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City</th>\n",
              "      <th>Zip Code</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Gender</th>\n",
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              "      <th>Phone Service</th>\n",
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              "      <th>Monthly Charges</th>\n",
              "      <th>Total Charges</th>\n",
              "      <th>Churn Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>90003</td>\n",
              "      <td>33.964131</td>\n",
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              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>90005</td>\n",
              "      <td>34.059281</td>\n",
              "      <td>-118.307420</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
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              "      <td>2</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",
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              "      <td>151.65</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>90006</td>\n",
              "      <td>34.048013</td>\n",
              "      <td>-118.293953</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>8</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>99.65</td>\n",
              "      <td>820.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>90010</td>\n",
              "      <td>34.062125</td>\n",
              "      <td>-118.315709</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>28</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>104.80</td>\n",
              "      <td>3046.05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>90015</td>\n",
              "      <td>34.039224</td>\n",
              "      <td>-118.266293</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>49</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>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>103.70</td>\n",
              "      <td>5036.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NOTE:** **XGBoost** accepts whitespace in the feature names. However, we cannot have any whitespace if we want to draw a tree. So, let's take care of that now by replacing the white space in the city names with an underscore character ```_```."
      ],
      "metadata": {
        "id": "BwUqHDvnDNIX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"City\"].replace(\" \", \"_\", regex=True, inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "6_klFR90B6hb",
        "outputId": "7f694b5f-0f10-4f6a-bedb-171b223c262b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          City  Zip Code   Latitude   Longitude  Gender Senior Citizen  \\\n",
              "0  Los_Angeles     90003  33.964131 -118.272783    Male             No   \n",
              "1  Los_Angeles     90005  34.059281 -118.307420  Female             No   \n",
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              "  Partner Dependents  Tenure Months Phone Service  ... Device Protection  \\\n",
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              "4      No        Yes             49           Yes  ...               Yes   \n",
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              "4           No          Yes              Yes  Month-to-month   \n",
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              "  Paperless Billing             Payment Method Monthly Charges Total Charges  \\\n",
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              "2               Yes           Electronic check           99.65         820.5   \n",
              "3               Yes           Electronic check          104.80       3046.05   \n",
              "4               Yes  Bank transfer (automatic)          103.70        5036.3   \n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"City\"].unique()[0:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tummGGVNFdF_",
        "outputId": "35f10322-bb5d-4275-e7ef-a615b158f58e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Los_Angeles', 'Beverly_Hills', 'Huntington_Park', 'Lynwood',\n",
              "       'Marina_Del_Rey', 'Inglewood', 'Santa_Monica', 'Torrance',\n",
              "       'Whittier', 'La_Habra'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We also need to eliminate the whitespace in the column names."
      ],
      "metadata": {
        "id": "Bati4FhuFngd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns = df.columns.str.replace(\" \", \"_\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "CS79EnF7FgS6",
        "outputId": "4951f157-74f7-40df-9f00-44c4abdb8482"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          City  Zip_Code   Latitude   Longitude  Gender Senior_Citizen  \\\n",
              "0  Los_Angeles     90003  33.964131 -118.272783    Male             No   \n",
              "1  Los_Angeles     90005  34.059281 -118.307420  Female             No   \n",
              "2  Los_Angeles     90006  34.048013 -118.293953  Female             No   \n",
              "3  Los_Angeles     90010  34.062125 -118.315709  Female             No   \n",
              "4  Los_Angeles     90015  34.039224 -118.266293    Male             No   \n",
              "\n",
              "  Partner Dependents  Tenure_Months Phone_Service  ... Device_Protection  \\\n",
              "0      No         No              2           Yes  ...                No   \n",
              "1      No        Yes              2           Yes  ...                No   \n",
              "2      No        Yes              8           Yes  ...               Yes   \n",
              "3     Yes        Yes             28           Yes  ...               Yes   \n",
              "4      No        Yes             49           Yes  ...               Yes   \n",
              "\n",
              "  Tech_Support Streaming_TV Streaming_Movies        Contract  \\\n",
              "0           No           No               No  Month-to-month   \n",
              "1           No           No               No  Month-to-month   \n",
              "2           No          Yes              Yes  Month-to-month   \n",
              "3          Yes          Yes              Yes  Month-to-month   \n",
              "4           No          Yes              Yes  Month-to-month   \n",
              "\n",
              "  Paperless_Billing             Payment_Method Monthly_Charges Total_Charges  \\\n",
              "0               Yes               Mailed check           53.85        108.15   \n",
              "1               Yes           Electronic check           70.70        151.65   \n",
              "2               Yes           Electronic check           99.65         820.5   \n",
              "3               Yes           Electronic check          104.80       3046.05   \n",
              "4               Yes  Bank transfer (automatic)          103.70        5036.3   \n",
              "\n",
              "  Churn_Value  \n",
              "0           1  \n",
              "1           1  \n",
              "2           1  \n",
              "3           1  \n",
              "4           1  \n",
              "\n",
              "[5 rows x 24 columns]"
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              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Tenure_Months</th>\n",
              "      <th>Phone_Service</th>\n",
              "      <th>...</th>\n",
              "      <th>Device_Protection</th>\n",
              "      <th>Tech_Support</th>\n",
              "      <th>Streaming_TV</th>\n",
              "      <th>Streaming_Movies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>Paperless_Billing</th>\n",
              "      <th>Payment_Method</th>\n",
              "      <th>Monthly_Charges</th>\n",
              "      <th>Total_Charges</th>\n",
              "      <th>Churn_Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Los_Angeles</td>\n",
              "      <td>90003</td>\n",
              "      <td>33.964131</td>\n",
              "      <td>-118.272783</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Los_Angeles</td>\n",
              "      <td>90005</td>\n",
              "      <td>34.059281</td>\n",
              "      <td>-118.307420</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</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>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Los_Angeles</td>\n",
              "      <td>90006</td>\n",
              "      <td>34.048013</td>\n",
              "      <td>-118.293953</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>8</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>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>99.65</td>\n",
              "      <td>820.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Los_Angeles</td>\n",
              "      <td>90010</td>\n",
              "      <td>34.062125</td>\n",
              "      <td>-118.315709</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>28</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>104.80</td>\n",
              "      <td>3046.05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Los_Angeles</td>\n",
              "      <td>90015</td>\n",
              "      <td>34.039224</td>\n",
              "      <td>-118.266293</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>49</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>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>103.70</td>\n",
              "      <td>5036.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Identidying missing data"
      ],
      "metadata": {
        "id": "qKg9WO-3GIux"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The biggest part of any data analysis project is making sure that the data are correctly formatted and fixing it when it is not.\n",
        "\n",
        "One thing that is relatively unique about **XGBoost** is that it has default behavior for missing data. So, all we have to do is identify missing values and make sure they are set to ```0```."
      ],
      "metadata": {
        "id": "a46oae0-GWYO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, let's see what sort of data is in each column."
      ],
      "metadata": {
        "id": "gvySApjCH04f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "liG57UGNFzK8",
        "outputId": "bca9398e-bd2b-4f76-9afe-4fd021366cfc"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "City                  object\n",
              "Zip_Code               int64\n",
              "Latitude             float64\n",
              "Longitude            float64\n",
              "Gender                object\n",
              "Senior_Citizen        object\n",
              "Partner               object\n",
              "Dependents            object\n",
              "Tenure_Months          int64\n",
              "Phone_Service         object\n",
              "Multiple_Lines        object\n",
              "Internet_Service      object\n",
              "Online_Security       object\n",
              "Online_Backup         object\n",
              "Device_Protection     object\n",
              "Tech_Support          object\n",
              "Streaming_TV          object\n",
              "Streaming_Movies      object\n",
              "Contract              object\n",
              "Paperless_Billing     object\n",
              "Payment_Method        object\n",
              "Monthly_Charges      float64\n",
              "Total_Charges         object\n",
              "Churn_Value            int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A lot of columns are ```object```, and this is fine, as we saw above when we ran ```head()``` there were a lot of text responses, like ```Yes``` and ```No```. Nevertheless, let's verify that we are getting what we expect. "
      ],
      "metadata": {
        "id": "VrFOEcAnIO8X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Phone_Service\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-l7X7-KAH5G7",
        "outputId": "8899e862-17d1-4070-f9a1-a46f316d2ba6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Yes', 'No'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"City\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxUkblijJOc4",
        "outputId": "ade68aa8-4c3a-4060-9cec-9581b14979f8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Los_Angeles', 'Beverly_Hills', 'Huntington_Park', ..., 'Standish',\n",
              "       'Tulelake', 'Olympic_Valley'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Zip_Code\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LoU-shvnJe8O",
        "outputId": "72b50740-194b-4a35-b363-53d268da0a09"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([90003, 90005, 90006, ..., 96128, 96134, 96146])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In practice, we should check every other column. For columns that have long lists of values, I nearly always stream the values to text files for further analysis. \n",
        "\n",
        "In this particular dataset, all the other columns are fine, except the **Total_Charges** column. **Total_Charges** looks like it contains numbers, not text, but the ```object``` data type suggests that it contains more than just numbers."
      ],
      "metadata": {
        "id": "B5X3JVONLrmS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Total_Charges\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q7UEJKR0MZhV",
        "outputId": "0413f45c-592e-4d6a-8a8b-e7acda6d1a26"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['108.15', '151.65', '820.5', ..., '7362.9', '346.45', '6844.5'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Check to determine issues in this column\n",
        "## df[\"Total_Charges\"] = pd.to_numeric(df[\"Total_Charges\"])"
      ],
      "metadata": {
        "id": "waDVbv8RUJPN"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are blank spaces, \" \", in the data. We need to deal with those."
      ],
      "metadata": {
        "id": "_Ws84tMlVE4W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dealing with missing data, XGBoost style"
      ],
      "metadata": {
        "id": "0yCUYjL-VZqY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we need to determine how many rows are missing data. If it is a lot, then we might have a problem on our hands that is bigger than what **XGBoost** can deal with on its own. If it is not that many, we can just set them to ```0```."
      ],
      "metadata": {
        "id": "UJvrntrbVvc5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(df.loc[df[\"Total_Charges\"] == \" \"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oAz9qaXgVDMl",
        "outputId": "cf8ace07-53df-42f9-cd34-a7a812bce610"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We only have 11 rows missing values. Let's look at them."
      ],
      "metadata": {
        "id": "SizhFS6_WvIz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"Total_Charges\"] == \" \"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "Bq2oEjOjWlkC",
        "outputId": "65b32236-4d98-4c40-fe43-7fad3c1bc2a8"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                City  Zip_Code   Latitude   Longitude  Gender Senior_Citizen  \\\n",
              "2234  San_Bernardino     92408  34.084909 -117.258107  Female             No   \n",
              "2438    Independence     93526  36.869584 -118.189241    Male             No   \n",
              "2568       San_Mateo     94401  37.590421 -122.306467  Female             No   \n",
              "2667       Cupertino     95014  37.306612 -122.080621    Male             No   \n",
              "2856        Redcrest     95569  40.363446 -123.835041  Female             No   \n",
              "4331     Los_Angeles     90029  34.089953 -118.294824    Male             No   \n",
              "4687        Sun_City     92585  33.739412 -117.173334    Male             No   \n",
              "5104      Ben_Lomond     95005  37.078873 -122.090386  Female             No   \n",
              "5719        La_Verne     91750  34.144703 -117.770299    Male             No   \n",
              "6772            Bell     90201  33.970343 -118.171368  Female             No   \n",
              "6840      Wilmington     90744  33.782068 -118.262263    Male             No   \n",
              "\n",
              "     Partner Dependents  Tenure_Months Phone_Service  ...  \\\n",
              "2234     Yes         No              0            No  ...   \n",
              "2438      No         No              0           Yes  ...   \n",
              "2568     Yes         No              0           Yes  ...   \n",
              "2667     Yes        Yes              0           Yes  ...   \n",
              "2856     Yes         No              0            No  ...   \n",
              "4331     Yes        Yes              0           Yes  ...   \n",
              "4687     Yes        Yes              0           Yes  ...   \n",
              "5104     Yes        Yes              0           Yes  ...   \n",
              "5719     Yes        Yes              0           Yes  ...   \n",
              "6772     Yes        Yes              0           Yes  ...   \n",
              "6840      No        Yes              0           Yes  ...   \n",
              "\n",
              "        Device_Protection         Tech_Support         Streaming_TV  \\\n",
              "2234                  Yes                  Yes                  Yes   \n",
              "2438  No internet service  No internet service  No internet service   \n",
              "2568                  Yes                   No                  Yes   \n",
              "2667  No internet service  No internet service  No internet service   \n",
              "2856                  Yes                  Yes                  Yes   \n",
              "4331  No internet service  No internet service  No internet service   \n",
              "4687  No internet service  No internet service  No internet service   \n",
              "5104  No internet service  No internet service  No internet service   \n",
              "5719  No internet service  No internet service  No internet service   \n",
              "6772                  Yes                  Yes                  Yes   \n",
              "6840                   No                  Yes                   No   \n",
              "\n",
              "         Streaming_Movies  Contract Paperless_Billing  \\\n",
              "2234                   No  Two year               Yes   \n",
              "2438  No internet service  Two year                No   \n",
              "2568                  Yes  Two year                No   \n",
              "2667  No internet service  Two year                No   \n",
              "2856                   No  Two year                No   \n",
              "4331  No internet service  Two year                No   \n",
              "4687  No internet service  Two year                No   \n",
              "5104  No internet service  Two year                No   \n",
              "5719  No internet service  One year               Yes   \n",
              "6772                   No  Two year                No   \n",
              "6840                   No  Two year               Yes   \n",
              "\n",
              "                 Payment_Method Monthly_Charges Total_Charges Churn_Value  \n",
              "2234  Bank transfer (automatic)           52.55                         0  \n",
              "2438               Mailed check           20.25                         0  \n",
              "2568               Mailed check           80.85                         0  \n",
              "2667               Mailed check           25.75                         0  \n",
              "2856    Credit card (automatic)           56.05                         0  \n",
              "4331               Mailed check           19.85                         0  \n",
              "4687               Mailed check           25.35                         0  \n",
              "5104               Mailed check           20.00                         0  \n",
              "5719               Mailed check           19.70                         0  \n",
              "6772               Mailed check           73.35                         0  \n",
              "6840  Bank transfer (automatic)           61.90                         0  \n",
              "\n",
              "[11 rows x 24 columns]"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City</th>\n",
              "      <th>Zip_Code</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Senior_Citizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Tenure_Months</th>\n",
              "      <th>Phone_Service</th>\n",
              "      <th>...</th>\n",
              "      <th>Device_Protection</th>\n",
              "      <th>Tech_Support</th>\n",
              "      <th>Streaming_TV</th>\n",
              "      <th>Streaming_Movies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>Paperless_Billing</th>\n",
              "      <th>Payment_Method</th>\n",
              "      <th>Monthly_Charges</th>\n",
              "      <th>Total_Charges</th>\n",
              "      <th>Churn_Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2234</th>\n",
              "      <td>San_Bernardino</td>\n",
              "      <td>92408</td>\n",
              "      <td>34.084909</td>\n",
              "      <td>-117.258107</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</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>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>52.55</td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2438</th>\n",
              "      <td>Independence</td>\n",
              "      <td>93526</td>\n",
              "      <td>36.869584</td>\n",
              "      <td>-118.189241</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</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></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2568</th>\n",
              "      <td>San_Mateo</td>\n",
              "      <td>94401</td>\n",
              "      <td>37.590421</td>\n",
              "      <td>-122.306467</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</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></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2667</th>\n",
              "      <td>Cupertino</td>\n",
              "      <td>95014</td>\n",
              "      <td>37.306612</td>\n",
              "      <td>-122.080621</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</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></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2856</th>\n",
              "      <td>Redcrest</td>\n",
              "      <td>95569</td>\n",
              "      <td>40.363446</td>\n",
              "      <td>-123.835041</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</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>Credit card (automatic)</td>\n",
              "      <td>56.05</td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4331</th>\n",
              "      <td>Los_Angeles</td>\n",
              "      <td>90029</td>\n",
              "      <td>34.089953</td>\n",
              "      <td>-118.294824</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</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></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4687</th>\n",
              "      <td>Sun_City</td>\n",
              "      <td>92585</td>\n",
              "      <td>33.739412</td>\n",
              "      <td>-117.173334</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</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></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5104</th>\n",
              "      <td>Ben_Lomond</td>\n",
              "      <td>95005</td>\n",
              "      <td>37.078873</td>\n",
              "      <td>-122.090386</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</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></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5719</th>\n",
              "      <td>La_Verne</td>\n",
              "      <td>91750</td>\n",
              "      <td>34.144703</td>\n",
              "      <td>-117.770299</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</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></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6772</th>\n",
              "      <td>Bell</td>\n",
              "      <td>90201</td>\n",
              "      <td>33.970343</td>\n",
              "      <td>-118.171368</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</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>Mailed check</td>\n",
              "      <td>73.35</td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6840</th>\n",
              "      <td>Wilmington</td>\n",
              "      <td>90744</td>\n",
              "      <td>33.782068</td>\n",
              "      <td>-118.262263</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>Yes</td>\n",
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              "      <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></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11 rows × 24 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Tenure_Month** being ```0``` indicates that all 11 people with ```Total_Charges == \" \"``` have just signed up. We have a few choices here, we can set **Total_Charges** to ```0``` for these 11 people or we can remove them. We proceed with the former option because we just have a handful of people having Total_Charges equal to ```0```."
      ],
      "metadata": {
        "id": "8ftaiHmVXzRY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"Total_Charges\"] == \" \", \"Total_Charges\"] = 0"
      ],
      "metadata": {
        "id": "5aX65Y2ZW8ht"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's verify the result after modifying **Total_Charges** by looking at customers who had **Tenure_Months** set to ```0```."
      ],
      "metadata": {
        "id": "b4eap-4kZmDF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"Tenure_Months\"] == 0]"
      ],
      "metadata": {
        "colab": {
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          "height": 644
        },
        "id": "ecXSxwkWZiLm",
        "outputId": "1570ed38-5c7c-497d-d288-2c9499c03f68"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                City  Zip_Code   Latitude   Longitude  Gender Senior_Citizen  \\\n",
              "2234  San_Bernardino     92408  34.084909 -117.258107  Female             No   \n",
              "2438    Independence     93526  36.869584 -118.189241    Male             No   \n",
              "2568       San_Mateo     94401  37.590421 -122.306467  Female             No   \n",
              "2667       Cupertino     95014  37.306612 -122.080621    Male             No   \n",
              "2856        Redcrest     95569  40.363446 -123.835041  Female             No   \n",
              "4331     Los_Angeles     90029  34.089953 -118.294824    Male             No   \n",
              "4687        Sun_City     92585  33.739412 -117.173334    Male             No   \n",
              "5104      Ben_Lomond     95005  37.078873 -122.090386  Female             No   \n",
              "5719        La_Verne     91750  34.144703 -117.770299    Male             No   \n",
              "6772            Bell     90201  33.970343 -118.171368  Female             No   \n",
              "6840      Wilmington     90744  33.782068 -118.262263    Male             No   \n",
              "\n",
              "     Partner Dependents  Tenure_Months Phone_Service  ...  \\\n",
              "2234     Yes         No              0            No  ...   \n",
              "2438      No         No              0           Yes  ...   \n",
              "2568     Yes         No              0           Yes  ...   \n",
              "2667     Yes        Yes              0           Yes  ...   \n",
              "2856     Yes         No              0            No  ...   \n",
              "4331     Yes        Yes              0           Yes  ...   \n",
              "4687     Yes        Yes              0           Yes  ...   \n",
              "5104     Yes        Yes              0           Yes  ...   \n",
              "5719     Yes        Yes              0           Yes  ...   \n",
              "6772     Yes        Yes              0           Yes  ...   \n",
              "6840      No        Yes              0           Yes  ...   \n",
              "\n",
              "        Device_Protection         Tech_Support         Streaming_TV  \\\n",
              "2234                  Yes                  Yes                  Yes   \n",
              "2438  No internet service  No internet service  No internet service   \n",
              "2568                  Yes                   No                  Yes   \n",
              "2667  No internet service  No internet service  No internet service   \n",
              "2856                  Yes                  Yes                  Yes   \n",
              "4331  No internet service  No internet service  No internet service   \n",
              "4687  No internet service  No internet service  No internet service   \n",
              "5104  No internet service  No internet service  No internet service   \n",
              "5719  No internet service  No internet service  No internet service   \n",
              "6772                  Yes                  Yes                  Yes   \n",
              "6840                   No                  Yes                   No   \n",
              "\n",
              "         Streaming_Movies  Contract Paperless_Billing  \\\n",
              "2234                   No  Two year               Yes   \n",
              "2438  No internet service  Two year                No   \n",
              "2568                  Yes  Two year                No   \n",
              "2667  No internet service  Two year                No   \n",
              "2856                   No  Two year                No   \n",
              "4331  No internet service  Two year                No   \n",
              "4687  No internet service  Two year                No   \n",
              "5104  No internet service  Two year                No   \n",
              "5719  No internet service  One year               Yes   \n",
              "6772                   No  Two year                No   \n",
              "6840                   No  Two year               Yes   \n",
              "\n",
              "                 Payment_Method Monthly_Charges Total_Charges Churn_Value  \n",
              "2234  Bank transfer (automatic)           52.55             0           0  \n",
              "2438               Mailed check           20.25             0           0  \n",
              "2568               Mailed check           80.85             0           0  \n",
              "2667               Mailed check           25.75             0           0  \n",
              "2856    Credit card (automatic)           56.05             0           0  \n",
              "4331               Mailed check           19.85             0           0  \n",
              "4687               Mailed check           25.35             0           0  \n",
              "5104               Mailed check           20.00             0           0  \n",
              "5719               Mailed check           19.70             0           0  \n",
              "6772               Mailed check           73.35             0           0  \n",
              "6840  Bank transfer (automatic)           61.90             0           0  \n",
              "\n",
              "[11 rows x 24 columns]"
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              "        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>City</th>\n",
              "      <th>Zip_Code</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Senior_Citizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Tenure_Months</th>\n",
              "      <th>Phone_Service</th>\n",
              "      <th>...</th>\n",
              "      <th>Device_Protection</th>\n",
              "      <th>Tech_Support</th>\n",
              "      <th>Streaming_TV</th>\n",
              "      <th>Streaming_Movies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>Paperless_Billing</th>\n",
              "      <th>Payment_Method</th>\n",
              "      <th>Monthly_Charges</th>\n",
              "      <th>Total_Charges</th>\n",
              "      <th>Churn_Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2234</th>\n",
              "      <td>San_Bernardino</td>\n",
              "      <td>92408</td>\n",
              "      <td>34.084909</td>\n",
              "      <td>-117.258107</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</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>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>52.55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2438</th>\n",
              "      <td>Independence</td>\n",
              "      <td>93526</td>\n",
              "      <td>36.869584</td>\n",
              "      <td>-118.189241</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2568</th>\n",
              "      <td>San_Mateo</td>\n",
              "      <td>94401</td>\n",
              "      <td>37.590421</td>\n",
              "      <td>-122.306467</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2667</th>\n",
              "      <td>Cupertino</td>\n",
              "      <td>95014</td>\n",
              "      <td>37.306612</td>\n",
              "      <td>-122.080621</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2856</th>\n",
              "      <td>Redcrest</td>\n",
              "      <td>95569</td>\n",
              "      <td>40.363446</td>\n",
              "      <td>-123.835041</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</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>Credit card (automatic)</td>\n",
              "      <td>56.05</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4331</th>\n",
              "      <td>Los_Angeles</td>\n",
              "      <td>90029</td>\n",
              "      <td>34.089953</td>\n",
              "      <td>-118.294824</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4687</th>\n",
              "      <td>Sun_City</td>\n",
              "      <td>92585</td>\n",
              "      <td>33.739412</td>\n",
              "      <td>-117.173334</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5104</th>\n",
              "      <td>Ben_Lomond</td>\n",
              "      <td>95005</td>\n",
              "      <td>37.078873</td>\n",
              "      <td>-122.090386</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5719</th>\n",
              "      <td>La_Verne</td>\n",
              "      <td>91750</td>\n",
              "      <td>34.144703</td>\n",
              "      <td>-117.770299</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6772</th>\n",
              "      <td>Bell</td>\n",
              "      <td>90201</td>\n",
              "      <td>33.970343</td>\n",
              "      <td>-118.171368</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</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>Mailed check</td>\n",
              "      <td>73.35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6840</th>\n",
              "      <td>Wilmington</td>\n",
              "      <td>90744</td>\n",
              "      <td>33.782068</td>\n",
              "      <td>-118.262263</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8da19a24-bd00-4f8d-b4f0-fdc4f6107a6b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8da19a24-bd00-4f8d-b4f0-fdc4f6107a6b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8da19a24-bd00-4f8d-b4f0-fdc4f6107a6b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Total_Charges** still has the object data type. That is no good because **XGBoost** only allows ```int```, ```float```, or ```boolean``` data types. We can fix this by converting it with ```to_numeric()```."
      ],
      "metadata": {
        "id": "rky_eGZlXAEg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Total_Charges\"] = pd.to_numeric(df[\"Total_Charges\"])\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L_-kkrCOaHZn",
        "outputId": "01eb8d7b-4367-4382-8d00-7deef0c99736"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "City                  object\n",
              "Zip_Code               int64\n",
              "Latitude             float64\n",
              "Longitude            float64\n",
              "Gender                object\n",
              "Senior_Citizen        object\n",
              "Partner               object\n",
              "Dependents            object\n",
              "Tenure_Months          int64\n",
              "Phone_Service         object\n",
              "Multiple_Lines        object\n",
              "Internet_Service      object\n",
              "Online_Security       object\n",
              "Online_Backup         object\n",
              "Device_Protection     object\n",
              "Tech_Support          object\n",
              "Streaming_TV          object\n",
              "Streaming_Movies      object\n",
              "Contract              object\n",
              "Paperless_Billing     object\n",
              "Payment_Method        object\n",
              "Monthly_Charges      float64\n",
              "Total_Charges        float64\n",
              "Churn_Value            int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import urllib.parse\n",
        "\n",
        "address = 'Los Angeles 90029'\n",
        "url = 'https://nominatim.openstreetmap.org/search/' + urllib.parse.quote(address) +'?format=json'\n",
        "\n",
        "response = requests.get(url).json()\n",
        "print(response[0][\"lat\"])\n",
        "print(response[0][\"lon\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5aP2wW5qvOa",
        "outputId": "463bd9f1-4b33-4243-95f4-0ef6d5b9ff8e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34.0536909\n",
            "-118.242766\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "np.set_printoptions(threshold=sys.maxsize)\n",
        "df[\"Tenure_Months\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZ8yYLV-l1WX",
        "outputId": "9e1b1ff6-526f-40e1-876a-8db43d876d81"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2,  8, 28, 49, 10,  1, 47, 17,  5, 34, 11, 15, 18,  9,  7, 12, 25,\n",
              "       68, 55, 37,  3, 27, 20,  4, 58, 53, 13,  6, 19, 59, 16, 52, 24, 32,\n",
              "       38, 54, 43, 63, 21, 69, 22, 61, 60, 48, 40, 23, 39, 35, 56, 65, 33,\n",
              "       30, 45, 46, 62, 70, 50, 44, 71, 26, 14, 41, 66, 64, 29, 42, 67, 51,\n",
              "       31, 57, 36, 72,  0])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have dealt with the missing data, we can replace all of the other whitespaces in all of the columns with underscores. Note that we are doing this so that we can draw a picture of the **XGBoost** trees."
      ],
      "metadata": {
        "id": "iAkhzrvKYzH4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.replace(\" \", \"_\", regex=True, inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "yMHWzyiSYalh",
        "outputId": "4f102b4a-1273-4ca5-de45-61bddd6beea2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          City  Zip_Code   Latitude   Longitude  Gender Senior_Citizen  \\\n",
              "0  Los_Angeles     90003  33.964131 -118.272783    Male             No   \n",
              "1  Los_Angeles     90005  34.059281 -118.307420  Female             No   \n",
              "2  Los_Angeles     90006  34.048013 -118.293953  Female             No   \n",
              "3  Los_Angeles     90010  34.062125 -118.315709  Female             No   \n",
              "4  Los_Angeles     90015  34.039224 -118.266293    Male             No   \n",
              "\n",
              "  Partner Dependents  Tenure_Months Phone_Service  ... Device_Protection  \\\n",
              "0      No         No              2           Yes  ...                No   \n",
              "1      No        Yes              2           Yes  ...                No   \n",
              "2      No        Yes              8           Yes  ...               Yes   \n",
              "3     Yes        Yes             28           Yes  ...               Yes   \n",
              "4      No        Yes             49           Yes  ...               Yes   \n",
              "\n",
              "  Tech_Support Streaming_TV Streaming_Movies        Contract  \\\n",
              "0           No           No               No  Month-to-month   \n",
              "1           No           No               No  Month-to-month   \n",
              "2           No          Yes              Yes  Month-to-month   \n",
              "3          Yes          Yes              Yes  Month-to-month   \n",
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              "  Paperless_Billing             Payment_Method Monthly_Charges Total_Charges  \\\n",
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              "0           1  \n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split the data into dependent and independent variables"
      ],
      "metadata": {
        "id": "Jd5YHbrfaPtA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are going to split the data into two parts:\n",
        "\n",
        "1. The columns of the data that we will use to make classifications, a.k.a. predictors,\n",
        "2. the column of the data that we want to predict, a.k.a. response variable.\n",
        "\n",
        "We are going to use the conventional notation of **```X```** to represent the predictors, and **```y```** to represent the response variable.\n",
        "\n"
      ],
      "metadata": {
        "id": "0MTboBTVcVxm"
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      "source": [
        "X = df.drop(\"Churn_Value\", axis=1).copy()\n",
        "X.head()"
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          "height": 386
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        "id": "OVIAQ0oYZyB4",
        "outputId": "4548d0c9-f000-4767-851e-86337df26298"
      },
      "execution_count": 24,
      "outputs": [
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              "          City  Zip_Code   Latitude   Longitude  Gender Senior_Citizen  \\\n",
              "0  Los_Angeles     90003  33.964131 -118.272783    Male             No   \n",
              "1  Los_Angeles     90005  34.059281 -118.307420  Female             No   \n",
              "2  Los_Angeles     90006  34.048013 -118.293953  Female             No   \n",
              "3  Los_Angeles     90010  34.062125 -118.315709  Female             No   \n",
              "4  Los_Angeles     90015  34.039224 -118.266293    Male             No   \n",
              "\n",
              "  Partner Dependents  Tenure_Months Phone_Service  ... Online_Backup  \\\n",
              "0      No         No              2           Yes  ...           Yes   \n",
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              "2      No        Yes              8           Yes  ...            No   \n",
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              "4      No        Yes             49           Yes  ...           Yes   \n",
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              "3               Yes          Yes          Yes              Yes   \n",
              "4               Yes           No          Yes              Yes   \n",
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              "0  Month-to-month               Yes               Mailed_check   \n",
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              "2  Month-to-month               Yes           Electronic_check   \n",
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              "4  Month-to-month               Yes  Bank_transfer_(automatic)   \n",
              "\n",
              "  Monthly_Charges Total_Charges  \n",
              "0           53.85        108.15  \n",
              "1           70.70        151.65  \n",
              "2           99.65        820.50  \n",
              "3          104.80       3046.05  \n",
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              "\n",
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              "          const element = document.querySelector('#df-bd581cc7-6b94-45c2-9340-ab8dcf84fb64');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df[\"Churn_Value\"].copy()\n",
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEbnhgaxarb-",
        "outputId": "7b889920-8483-40fb-fd79-96724e219978"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    1\n",
              "Name: Churn_Value, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One-hot encoding"
      ],
      "metadata": {
        "id": "B0sjkuuibwjw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**XGBoost** natively supports continuous data, like **Monthly_Charges** and **Total_Charges**, it does not natively support categorical data, like **Phone_Service**, which contains 2 different categories. We need to convert a column of categorical data into multiple columns of binary values using a technique called **One-hot encoding**.\n",
        "\n",
        "The reason we don't use continuous data in this case is that the **XGBoost** tree would be more likely to cluster the data samples that have small distances to each other than the data samples that have large distances to each other.\n",
        "\n",
        "Before we commit to converting columns with **One-hot encoding**, let's see what happens when we convert **Payment_Method** without saving the results."
      ],
      "metadata": {
        "id": "Bu2QOKRVR4oq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.get_dummies(X, columns=[\"Payment_Method\"]).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "cPDecCCLbeDX",
        "outputId": "5b19aca8-c348-4f1b-a5a4-75aaef3e8892"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          City  Zip_Code   Latitude   Longitude  Gender Senior_Citizen  \\\n",
              "0  Los_Angeles     90003  33.964131 -118.272783    Male             No   \n",
              "1  Los_Angeles     90005  34.059281 -118.307420  Female             No   \n",
              "2  Los_Angeles     90006  34.048013 -118.293953  Female             No   \n",
              "3  Los_Angeles     90010  34.062125 -118.315709  Female             No   \n",
              "4  Los_Angeles     90015  34.039224 -118.266293    Male             No   \n",
              "\n",
              "  Partner Dependents  Tenure_Months Phone_Service  ... Streaming_TV  \\\n",
              "0      No         No              2           Yes  ...           No   \n",
              "1      No        Yes              2           Yes  ...           No   \n",
              "2      No        Yes              8           Yes  ...          Yes   \n",
              "3     Yes        Yes             28           Yes  ...          Yes   \n",
              "4      No        Yes             49           Yes  ...          Yes   \n",
              "\n",
              "  Streaming_Movies        Contract Paperless_Billing Monthly_Charges  \\\n",
              "0               No  Month-to-month               Yes           53.85   \n",
              "1               No  Month-to-month               Yes           70.70   \n",
              "2              Yes  Month-to-month               Yes           99.65   \n",
              "3              Yes  Month-to-month               Yes          104.80   \n",
              "4              Yes  Month-to-month               Yes          103.70   \n",
              "\n",
              "  Total_Charges Payment_Method_Bank_transfer_(automatic)  \\\n",
              "0        108.15                                        0   \n",
              "1        151.65                                        0   \n",
              "2        820.50                                        0   \n",
              "3       3046.05                                        0   \n",
              "4       5036.30                                        1   \n",
              "\n",
              "  Payment_Method_Credit_card_(automatic) Payment_Method_Electronic_check  \\\n",
              "0                                      0                               0   \n",
              "1                                      0                               1   \n",
              "2                                      0                               1   \n",
              "3                                      0                               1   \n",
              "4                                      0                               0   \n",
              "\n",
              "  Payment_Method_Mailed_check  \n",
              "0                           1  \n",
              "1                           0  \n",
              "2                           0  \n",
              "3                           0  \n",
              "4                           0  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
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              "      <td>-118.315709</td>\n",
              "      <td>Female</td>\n",
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              "      <td>49</td>\n",
              "      <td>Yes</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessing = make_column_transformer(\n",
        "    (\"passthrough\", make_column_selector(dtype_include=np.number)),\n",
        "    (OneHotEncoder(handle_unknown=\"ignore\", sparse=False), make_column_selector(dtype_include=object)),\n",
        ")\n",
        "X_transformed = preprocessing.fit_transform(X)\n",
        "X_encoded = pd.DataFrame(data=X_transformed,\n",
        "                         index=X.index,\n",
        "                         columns=preprocessing.get_feature_names_out(),\n",
        "                         )"
      ],
      "metadata": {
        "id": "gYi57qFVYJ3J"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_encoded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "8YpQRUQjTkOM",
        "outputId": "4fbd4c39-de36-482a-c790-f9f1dbac0108"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      passthrough__Zip_Code  passthrough__Latitude  passthrough__Longitude  \\\n",
              "0                   90003.0              33.964131             -118.272783   \n",
              "1                   90005.0              34.059281             -118.307420   \n",
              "2                   90006.0              34.048013             -118.293953   \n",
              "3                   90010.0              34.062125             -118.315709   \n",
              "4                   90015.0              34.039224             -118.266293   \n",
              "...                     ...                    ...                     ...   \n",
              "7038                92285.0              34.341737             -116.539416   \n",
              "7039                92301.0              34.667815             -117.536183   \n",
              "7040                92304.0              34.559882             -115.637164   \n",
              "7041                92305.0              34.167800             -116.864330   \n",
              "7042                92308.0              34.424926             -117.184503   \n",
              "\n",
              "      passthrough__Tenure_Months  passthrough__Monthly_Charges  \\\n",
              "0                            2.0                         53.85   \n",
              "1                            2.0                         70.70   \n",
              "2                            8.0                         99.65   \n",
              "3                           28.0                        104.80   \n",
              "4                           49.0                        103.70   \n",
              "...                          ...                           ...   \n",
              "7038                        72.0                         21.15   \n",
              "7039                        24.0                         84.80   \n",
              "7040                        72.0                        103.20   \n",
              "7041                        11.0                         29.60   \n",
              "7042                        66.0                        105.65   \n",
              "\n",
              "      passthrough__Total_Charges  onehotencoder__City_Acampo  \\\n",
              "0                         108.15                         0.0   \n",
              "1                         151.65                         0.0   \n",
              "2                         820.50                         0.0   \n",
              "3                        3046.05                         0.0   \n",
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              "\n",
              "      onehotencoder__City_Acton  onehotencoder__City_Adelanto  \\\n",
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              "\n",
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              "1                          0.0  ...                                  0.0   \n",
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              "7041                       0.0  ...                                  0.0   \n",
              "7042                       0.0  ...                                  1.0   \n",
              "\n",
              "      onehotencoder__Contract_Month-to-month  \\\n",
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              "1                                        1.0   \n",
              "2                                        1.0   \n",
              "3                                        1.0   \n",
              "4                                        1.0   \n",
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              "7041                               0.0                               0.0   \n",
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              "\n",
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              "\n",
              "      onehotencoder__Payment_Method_Bank_transfer_(automatic)  \\\n",
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              "7042                                                1.0         \n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0fa3cb49-3717-4163-8b7f-bc41c56b3c95 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0fa3cb49-3717-4163-8b7f-bc41c56b3c95');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's verify if y only contains **1s** and **0s**."
      ],
      "metadata": {
        "id": "bhKjzipfatKu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ohq0vUcKaO7n",
        "outputId": "f50114e9-a8ed-4b1b-cae5-bc62756f8875"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Build a baseline XGBoost model"
      ],
      "metadata": {
        "id": "b9wWsVFDbzEr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This dataset is in fact imbalanced. It is proved by the following number:"
      ],
      "metadata": {
        "id": "wt0pRgK9es1K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sum(y) / len(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kq1sXyf2a4fa",
        "outputId": "8014c5c4-2856-499b-c65a-90fdd370e705"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2653698707936959"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that only **27%** of the people in the dataset left the company. Because of this, when we split the data into training and testing, we will split using stratification in order to maintain the same percentage of the people who left the company in both the training set and the testing set."
      ],
      "metadata": {
        "id": "F6Qq4WG9e5Eh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, \n",
        "                                                    random_state=42,\n",
        "                                                    stratify=y)"
      ],
      "metadata": {
        "id": "zaEjaVKxe3qH"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's verify if using ```stratify``` worked as expected."
      ],
      "metadata": {
        "id": "B_WvwTvHfqye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sum(y_train) / len(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qi21PYTHfpXC",
        "outputId": "38583327-4a1c-468b-baea-717c0b98e2c4"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2654297614539947"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(y_test) / len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_wbH6GOBfzcB",
        "outputId": "9b5c10ff-c888-47cf-bd4a-b3b34b04cf2a"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.26519023282226006"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```stratify``` worked as expected and both ```y_train``` and ```y_test``` have the same percentage of people that left the company."
      ],
      "metadata": {
        "id": "1egSBvDUzSU6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf_xgb = xgb.XGBClassifier(objective=\"binary:logistic\",\n",
        "                            eval_metric=\"aucpr\",\n",
        "                            early_stopping_rounds=10,\n",
        "                            base_score=0.5,\n",
        "                            booster=\"gbtree\",\n",
        "                            colsample_bynode=1,\n",
        "                            colsample_bytree=1,\n",
        "                            seed=42)\n",
        "clf_xgb.fit(X_train,\n",
        "            y_train,\n",
        "            verbose=True,\n",
        "            eval_set=[(X_test, y_test)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CmUjmVIf3CM",
        "outputId": "bc1b5771-fd0f-4690-c967-1fe0236463d2"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-aucpr:0.64036\n",
            "[1]\tvalidation_0-aucpr:0.64404\n",
            "[2]\tvalidation_0-aucpr:0.65124\n",
            "[3]\tvalidation_0-aucpr:0.65052\n",
            "[4]\tvalidation_0-aucpr:0.65321\n",
            "[5]\tvalidation_0-aucpr:0.64854\n",
            "[6]\tvalidation_0-aucpr:0.65459\n",
            "[7]\tvalidation_0-aucpr:0.65895\n",
            "[8]\tvalidation_0-aucpr:0.65746\n",
            "[9]\tvalidation_0-aucpr:0.65850\n",
            "[10]\tvalidation_0-aucpr:0.66217\n",
            "[11]\tvalidation_0-aucpr:0.66527\n",
            "[12]\tvalidation_0-aucpr:0.66322\n",
            "[13]\tvalidation_0-aucpr:0.66310\n",
            "[14]\tvalidation_0-aucpr:0.66000\n",
            "[15]\tvalidation_0-aucpr:0.66027\n",
            "[16]\tvalidation_0-aucpr:0.65781\n",
            "[17]\tvalidation_0-aucpr:0.65593\n",
            "[18]\tvalidation_0-aucpr:0.65738\n",
            "[19]\tvalidation_0-aucpr:0.65829\n",
            "[20]\tvalidation_0-aucpr:0.65683\n",
            "[21]\tvalidation_0-aucpr:0.65707\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=1, colsample_bytree=1,\n",
              "              early_stopping_rounds=10, enable_categorical=False,\n",
              "              eval_metric='aucpr', feature_types=None, gamma=None, gpu_id=None,\n",
              "              grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_estimator(clf_xgb,\n",
        "                                      X_test,\n",
        "                                      y_test,\n",
        "                                      values_format=\"d\",\n",
        "                                      display_labels=[\"Did not leave\", \"Left\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "GSx5uaxBDDhR",
        "outputId": "9ca6490f-7779-4a19-a632-29499da50878"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f2604a88700>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the confusion matrix, we see that of the **1294** people that **did not leave**, **1166 (90%)** were correctly classified. And of the **467** people that **left the company**, **256 (55%)** were correctly classified. So this **XGBoost** model version was not awsome. Part of the problem is that our data is imbalanced. People leaving costs the company a lot of money, we would like to capture more of the people that left. The good news is XGBoost has a parameter ```scale_pos_weight```that helps with imbalanced data. "
      ],
      "metadata": {
        "id": "YFymcmKyGN5i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Optimize parameters using Cross Validation and GridSearch()"
      ],
      "metadata": {
        "id": "Rv7cORYlJoR9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # ROUND 1\n",
        "# param_grid = {\n",
        "#     \"max_depth\": [3, 4, 5],\n",
        "#     \"learn_rate\": [0.1, 0.01, 0.05],\n",
        "#     \"gamma\": [0, 0.25, 1.0],\n",
        "#     \"reg_lambda\": [0, 1.0, 10.0],\n",
        "#     \"scale_pos_weight\": [1, 3, 5]\n",
        "# }\n",
        "\n",
        "# ROUND 2\n",
        "# param_grid = {\n",
        "#     \"max_depth\": [5],\n",
        "#     \"learning_rate\": [0.05, 0.75, 1],\n",
        "#     \"gamma\": [1.0],\n",
        "#     \"reg_lambda\": [10, 20, 100],\n",
        "#     \"scale_pos_weight\": [1]\n",
        "# }\n",
        "\n",
        "# optimal_params = GridSearchCV(\n",
        "#     estimator=xgb.XGBClassifier(objective=\"binary:logistic\",\n",
        "#                                 seed=42,\n",
        "#                                 subsample=0.9,\n",
        "#                                 colsample_bytree=0.5,\n",
        "#                                 early_stopping_rounds=10,\n",
        "#                                 eval_metric=\"aucpr\"),\n",
        "#     param_grid=param_grid,\n",
        "#     scoring=\"roc_auc\",\n",
        "#     verbose=2,\n",
        "#     n_jobs=10,\n",
        "#     cv=3\n",
        "# )\n",
        "\n",
        "# optimal_params.fit(X_train,\n",
        "#                    y_train,\n",
        "#                    eval_set=[(X_test, y_test)],\n",
        "#                    verbose=False)\n",
        "# print(optimal_params.best_params_)"
      ],
      "metadata": {
        "id": "pz9YhjKeDRMp"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building, evaluating, drawing and interpreting the optimized XGBoost model"
      ],
      "metadata": {
        "id": "MgZwe1gHjeQM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we have the ideal parameter values, we can build the final **XGBoost** model."
      ],
      "metadata": {
        "id": "XrEkz_HVleLB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf_xgb = xgb.XGBClassifier(seed=42,\n",
        "                            objective=\"binary:logistic\",\n",
        "                            gamma=1.0,\n",
        "                            learning_rate=0.05,\n",
        "                            max_depth=5,\n",
        "                            reg_lambda=10,\n",
        "                            scale_pos_weight=3,\n",
        "                            subsample=0.9,\n",
        "                            colsample_bytree=0.5,\n",
        "                            early_stopping_rounds=10,\n",
        "                            eval_metric=\"aucpr\")\n",
        "clf_xgb.fit(X_train,\n",
        "            y_train,\n",
        "            verbose=True,\n",
        "            eval_set=[(X_test, y_test)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V2iH4e1wNonf",
        "outputId": "55ed3ff2-04d4-43ba-8403-d7818afadbc9"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-aucpr:0.51397\n",
            "[1]\tvalidation_0-aucpr:0.55169\n",
            "[2]\tvalidation_0-aucpr:0.60780\n",
            "[3]\tvalidation_0-aucpr:0.62136\n",
            "[4]\tvalidation_0-aucpr:0.62610\n",
            "[5]\tvalidation_0-aucpr:0.62063\n",
            "[6]\tvalidation_0-aucpr:0.62972\n",
            "[7]\tvalidation_0-aucpr:0.62828\n",
            "[8]\tvalidation_0-aucpr:0.63184\n",
            "[9]\tvalidation_0-aucpr:0.63297\n",
            "[10]\tvalidation_0-aucpr:0.63577\n",
            "[11]\tvalidation_0-aucpr:0.63805\n",
            "[12]\tvalidation_0-aucpr:0.63858\n",
            "[13]\tvalidation_0-aucpr:0.63562\n",
            "[14]\tvalidation_0-aucpr:0.63606\n",
            "[15]\tvalidation_0-aucpr:0.63686\n",
            "[16]\tvalidation_0-aucpr:0.63681\n",
            "[17]\tvalidation_0-aucpr:0.63725\n",
            "[18]\tvalidation_0-aucpr:0.63815\n",
            "[19]\tvalidation_0-aucpr:0.64025\n",
            "[20]\tvalidation_0-aucpr:0.64044\n",
            "[21]\tvalidation_0-aucpr:0.64067\n",
            "[22]\tvalidation_0-aucpr:0.64768\n",
            "[23]\tvalidation_0-aucpr:0.64911\n",
            "[24]\tvalidation_0-aucpr:0.65410\n",
            "[25]\tvalidation_0-aucpr:0.65373\n",
            "[26]\tvalidation_0-aucpr:0.65396\n",
            "[27]\tvalidation_0-aucpr:0.65297\n",
            "[28]\tvalidation_0-aucpr:0.65472\n",
            "[29]\tvalidation_0-aucpr:0.65345\n",
            "[30]\tvalidation_0-aucpr:0.65395\n",
            "[31]\tvalidation_0-aucpr:0.65451\n",
            "[32]\tvalidation_0-aucpr:0.65361\n",
            "[33]\tvalidation_0-aucpr:0.65416\n",
            "[34]\tvalidation_0-aucpr:0.65411\n",
            "[35]\tvalidation_0-aucpr:0.65565\n",
            "[36]\tvalidation_0-aucpr:0.65592\n",
            "[37]\tvalidation_0-aucpr:0.65597\n",
            "[38]\tvalidation_0-aucpr:0.65648\n",
            "[39]\tvalidation_0-aucpr:0.65546\n",
            "[40]\tvalidation_0-aucpr:0.65657\n",
            "[41]\tvalidation_0-aucpr:0.65487\n",
            "[42]\tvalidation_0-aucpr:0.65650\n",
            "[43]\tvalidation_0-aucpr:0.65607\n",
            "[44]\tvalidation_0-aucpr:0.65693\n",
            "[45]\tvalidation_0-aucpr:0.65831\n",
            "[46]\tvalidation_0-aucpr:0.65622\n",
            "[47]\tvalidation_0-aucpr:0.65626\n",
            "[48]\tvalidation_0-aucpr:0.65707\n",
            "[49]\tvalidation_0-aucpr:0.65660\n",
            "[50]\tvalidation_0-aucpr:0.65694\n",
            "[51]\tvalidation_0-aucpr:0.65778\n",
            "[52]\tvalidation_0-aucpr:0.65773\n",
            "[53]\tvalidation_0-aucpr:0.65793\n",
            "[54]\tvalidation_0-aucpr:0.65767\n",
            "[55]\tvalidation_0-aucpr:0.65867\n",
            "[56]\tvalidation_0-aucpr:0.65868\n",
            "[57]\tvalidation_0-aucpr:0.65967\n",
            "[58]\tvalidation_0-aucpr:0.66015\n",
            "[59]\tvalidation_0-aucpr:0.65985\n",
            "[60]\tvalidation_0-aucpr:0.65988\n",
            "[61]\tvalidation_0-aucpr:0.65981\n",
            "[62]\tvalidation_0-aucpr:0.65900\n",
            "[63]\tvalidation_0-aucpr:0.65901\n",
            "[64]\tvalidation_0-aucpr:0.65930\n",
            "[65]\tvalidation_0-aucpr:0.66084\n",
            "[66]\tvalidation_0-aucpr:0.66115\n",
            "[67]\tvalidation_0-aucpr:0.66061\n",
            "[68]\tvalidation_0-aucpr:0.66013\n",
            "[69]\tvalidation_0-aucpr:0.66038\n",
            "[70]\tvalidation_0-aucpr:0.66095\n",
            "[71]\tvalidation_0-aucpr:0.66145\n",
            "[72]\tvalidation_0-aucpr:0.66129\n",
            "[73]\tvalidation_0-aucpr:0.66147\n",
            "[74]\tvalidation_0-aucpr:0.66186\n",
            "[75]\tvalidation_0-aucpr:0.66061\n",
            "[76]\tvalidation_0-aucpr:0.66077\n",
            "[77]\tvalidation_0-aucpr:0.66216\n",
            "[78]\tvalidation_0-aucpr:0.66243\n",
            "[79]\tvalidation_0-aucpr:0.66207\n",
            "[80]\tvalidation_0-aucpr:0.66249\n",
            "[81]\tvalidation_0-aucpr:0.66304\n",
            "[82]\tvalidation_0-aucpr:0.66285\n",
            "[83]\tvalidation_0-aucpr:0.66353\n",
            "[84]\tvalidation_0-aucpr:0.66303\n",
            "[85]\tvalidation_0-aucpr:0.66409\n",
            "[86]\tvalidation_0-aucpr:0.66407\n",
            "[87]\tvalidation_0-aucpr:0.66419\n",
            "[88]\tvalidation_0-aucpr:0.66399\n",
            "[89]\tvalidation_0-aucpr:0.66463\n",
            "[90]\tvalidation_0-aucpr:0.66488\n",
            "[91]\tvalidation_0-aucpr:0.66473\n",
            "[92]\tvalidation_0-aucpr:0.66500\n",
            "[93]\tvalidation_0-aucpr:0.66520\n",
            "[94]\tvalidation_0-aucpr:0.66548\n",
            "[95]\tvalidation_0-aucpr:0.66594\n",
            "[96]\tvalidation_0-aucpr:0.66587\n",
            "[97]\tvalidation_0-aucpr:0.66526\n",
            "[98]\tvalidation_0-aucpr:0.66548\n",
            "[99]\tvalidation_0-aucpr:0.66560\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.5, early_stopping_rounds=10,\n",
              "              enable_categorical=False, eval_metric='aucpr', feature_types=None,\n",
              "              gamma=1.0, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_estimator(clf_xgb,\n",
        "                                      X_test,\n",
        "                                      y_test,\n",
        "                                      values_format=\"d\",\n",
        "                                      display_labels=[\"Did not leave\", \"Left\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "1Ybou75aqoMG",
        "outputId": "d1d90a91-de2e-4926-92d9-1bbc6cbca9fb"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f94165a2dc0>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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AbmZWQkS+LacrgHsiYk9gX+B54DxgZkSMBGamY8gWkx6ZtsnANcUKbrTLRdJVFOk6iohzclffzKxCNedcLpJ6AIcDnwOIiI3ARkknAGNTtqnAQ8B/ACcA09JSdI+n1v2AxhaKLtaHPqs53oCZWUULIH9A7yupMHZOiYgpBcfDgGXALyTtC8wGzgX6FwTp14H+aX8gsLDg+kUprWkBPSKmFh5L6hoR60q/HzOz6tKE7pTlETG6yPn2wP7A2RHxhKQr2NK9kl4rQtI2Dawp2YeeOuyfA/6WjveVdPW2vJiZWeURUZdvy2ERsCginkjHt5AF+DckDQBIP5em84uBwQXXD0ppDcpzU/RHwMeBFQAR8TRZH5CZWdsQObdSxUS8DiyUtEdKGgc8B8wAJqa0icBtaX8GMCGNdhkDrGms/xxyjkOPiIXSP3z61Oa5zsys4kWzP/p/NvBrSR2Bl4HTyRrXN0maBCwATk557wKOAeYB61LeRuUJ6AslHQKEpA5kHfjPb8u7MDOrSM34qGhEPAU01M8+roG8AZyVt+w8XS5npAIHAq8Bo5ryAmZmlU85t/Iq2UKPiOXAaTugLmZmrVNduSuQT55RLsMl3S5pmaSlkm6TNHxHVM7MrOzqx6Hn2cosT5fLDcBNwABgN+Bm4DctWSkzs9akmR/9bzF5AnrXiLg+Ijan7VdA55aumJlZq9FMwxZbWrG5XHqn3bslnQfcSFblT5MNpTEzaxtaQXdKHsVuis4mC+D17+SLBecC+FZLVcrMrDXZtgfxd7xic7kM25EVMTNrlUJQIQtc5HpSVNLewF4U9J1HxLSWqpSZWatS6S30epIuIJundy+yvvOjgT8CDuhm1jZUSEDPM8rlRLJHUl+PiNPJVtjo0aK1MjNrTSp9lEuB9RFRJ2mzpJ3JpnUcXOoiM7Oq0LQFLsoqT0CfJakn8DOykS9rgcdaslJmZq1JxY9yqRcRZ6bdn0q6B9g5Iua0bLXMzFqRSg/okvYvdi4inmyZKpmZtS7V0EL/7yLnAjiymevS5rwwpysf321UuathTbDhrDzjCKy1iHbNVVCF96FHxBE7siJmZq1SM49gkfQK8BbZym+bI2J0mmplOjAUeAU4OSJWKVsq7gqyVYvWAZ8r1jvi5oaZWSnNP2zxiIgYFRH1KxedB8yMiJHAzHQM2XM/I9M2GbimWKEO6GZmJagu37YdTgCmpv2pwPiC9GmReRzoKWlAY4U4oJuZlZK/hd5X0qyCbXIjpd0raXbB+f4RsSTtvw70T/sDgYUF1y5KaQ3K8+i/yJagGx4RF0kaAuwaEX8uda2ZWaVTNGmUy/KCbpTGfDgiFkvaBbhP0t8KT0ZESNs2riZPC/1q4GDgM+n4LeAn2/JiZmYVqRmXoIuIxennUuBW4EDgjfqulPRzacq+mH98Mn9QSmtQnoB+UEScBbyTKrEK6Jir5mZm1aCZbopK6iZpp/p94GPAXGAGMDFlmwjclvZnABOUGQOsKeiaeY88j/5vktSuvrqS+lExa2CbmW2/ZnywqD9wa9aTTXvghoi4R9JfgJskTQIWACen/HeRDVmcRzZs8fRihecJ6FeSfS3YRdL3yWZf/M42vBEzs8oT2z2CZUtRES+TzVi7dfoKslltt04P4Ky85eeZy+XXkmanFxMwPiKez/sCZmYVrwoe/QcgjWpZB9xemBYRr7ZkxczMWo1qCejAnWxZLLozMAz4O/DBFqyXmVmrUQ2TcwEQEf9UeJxmYTyzkexmZlYmuRaJLhQRT0o6qCUqY2bWKlVLC13S1woOa4D9gddarEZmZq1JM45yaWl5Wug7FexvJutT/23LVMfMrBWqhhZ6eqBop4j4+g6qj5lZqyKq4KaopPYRsVnSoTuyQmZmrU6lB3Tgz2T95U9JmgHcDLxdfzIiftfCdTMzK7+mzbZYVnn60DsDK8jWEK0fjx6AA7qZtQ1VcFN0lzTCZS5bAnm9Cvm8MjPbftXQQm8HdOcfA3m9Cnl7ZmbNoEIiXrGAviQiLtphNTEza42avgB02RQL6PmW3zAzq3LV0OXynrl5zczapAoJ6I0uQRcRK3dkRczMWivV5dtylSW1k/RXSXek42GSnpA0T9J0SR1Teqd0PC+dH1qq7DxripqZtV151xPN34o/FyhcJOgS4PKI2B1YBUxK6ZOAVSn98pSvKAd0M7Mi1IStZFnSIOBY4Np0LLJnfG5JWaYC49P+CemYdH5cyt8oB3Qzs1Lyt9D7SppVsE3eqqQfAd9ky6NKfYDVEbE5HS8CBqb9gcBCgHR+TcrfqCbPh25m1tY0YZTL8ogY3WAZ0nHA0oiYLWls89TsHzmgm5mV0jyjXA4FPiHpGLIpVXYGrgB61k+GCAwCFqf8i4HBwCJJ7YEeZNOwNMpdLmZmxUTzjHKJiG9FxKCIGAqcAjwQEacBDwInpmwTgdvS/ox0TDr/QEQU/WhxQDczK6V5R7ls7T+Ar0maR9ZHfl1Kvw7ok9K/BpxXqiB3uZiZldDcT4pGxEPAQ2n/ZeDABvK8A5zUlHId0M3MSqmQJ0Ud0M3MSqiGuVzMzCyoigUuzMzavKpYJNrMzBIHdDOz6qDiw79bDQd0M7NiqmTFIjMzw33oZmZVI+/iFeXmgG5mVopb6GZmVSDc5WJmVj0c0M3MKp8fLDIzqyKqq4yI7oBuZlaMx6FbWzFoxDt8+6cL3j3edchGrr90V57+U3fOvngRXbrV8caijlxy1hDWrW1Xxpq2XR3bbea6CbfRsV0t7WrquP9vw/npwwdy4NBFfGXcY9QoWLexAxfcfiQLV/Vg153f4qLjH2CnzhupUR1XPTiGP770vnK/jbLysMUKJ2ltRHTPmbcfcAfQETgH+KeIuLol69daLHqpM2d+dA8AamqCXz/5HI/e3YPv/OwVfnbRbjzzeHc+dsoKTvzSUqZdOqDMtW2bNta2Y/KvPsH6TR1oX1PLzyf8nkfnDeHbRz3MV28+mvkrenHSAXP5/KGzueCOI/n8h2dz3/MjuPnJvRnedyVXffoujv1J2w7ozdVCl9QZeBjoRBZ/b4mICyQNA24kW7FoNvCvEbFRUidgGnAA2Xqin46IVxor30vQNY9xwDMRsR+wEDizzPUpi1GHrWXJgo4sXdyRQcM38Mzj3QD468M78eFj15S5dm2ZWL+pAwDta+po366OQATQrdNGAHbqtJFla7sCECG6ddoEQPeC9LZMkW/LYQNwZETsC4wCjpI0BrgEuDwidgdWAZNS/knAqpR+ecrXKLfQm0DSCOAnQD9gHfAFstW7fwB0kTQa+DswQtJTwH0R8Y0yVXeHG3vCKh76fS8AFrzQmYOPepPH7unBYcetod9um8pcu7atRnXcMOkWBvdaw/RZezP3tf5cdOdYrvr0nWzY3J63N3Rkwi8/BcD/PDKaqz9zB6eMfoYuHTZxxg2fKHPtyyyAZpqcKy3yvDYddkhbAEcCp6b0qcCFwDXACWkf4Bbgx5LU2GLRbqE3zRTg7Ig4APg6cHVEPAWcD0yPiFFkC76+FBGjGgrmkiZLmiVp1iY27MCqt6z2HeoY87E3efj2HgBc9rXBHD9xOT++5wW6dK9l80aVuYZtW13UcMq1J/PxKyew925LGdFvBacdOIezpx/LUVdN4LY5e/DvH30UgKP2msftc/bgqKsmcPb0Y/neJ2am9nzbpbp8G9C3/v932ia/pyypXWrwLQXuA14CVkfE5pRlETAw7Q8k+9ZPOr+GrFumQW6h5ySpO3AIcLP0bnDq1NRyImIK2QcDO6t31fwv+dCRbzHvmS6sXp59tV84rzPf/swIAAYO38BB494sZ/UsWbuhE7MWDOTQEa/y/v4rmPtafwDufW53fnLKnQCMH/U8Z/3mOADmLN6Vju0307Prelata5tdL00ch748IkYXyxARtcAoST2BW4E9t6d+hdxCz6+G7FN0VMH2gXJXqrUYO371u90tAD36ZF0sUnDquW9wx/WNNiqshfXqup7unbJvg53ab+agYQuZv7wX3TttZEjv1QCMGbaI+St6AvD6m905cNgiAIb1WUWn9rWsWtelHFVvHSLyb00qNlYDDwIHAz0l1TewBwGL0/5iYDBAOt+D7OZog9xCzyki3pQ0X9JJEXGzsmb6PhHx9FZZ3wJ2KkMVy6ZTl1r2P+wtrvjmoHfTjhi/muM/txyAR+/uwb039i5X9dq8vt3XcdHxD1CjOmoU3Pf87jwybyj/ddc/88N/+QMR4s13OnHhHUcAcNn9h/Cfx/wvnz1wDgGcf/uRZO3Utqu5nhRNI+I2RcRqSV2Aj5Ld6HwQOJFspMtE4LZ0yYx0/Fg6/0Bj/edZPStkJY4dTVId8FpB0mVkX4+uAQaQ3cy4MSIukvQ5YHREfDldewOwD3B3sZuiO6t3HKRxLfQOrCUsPeuQclfBmuDF6ZexbunC7fo02qnnoNjv8HNz5X3k9m/OLtblImkfspue7ci+9d+UYshwsmDeG/gr8NmI2JCGOV4P7AesBE6JiJcbK98t9EZERGPdUUc1kPeXwC8Ljk/dOo+ZVa7maqFHxByy4Lx1+svAgQ2kvwOclLd8B3Qzs2ICqK2MngwHdDOzEjzboplZtaiQe40O6GZmJbiFbmZWDTx9rplZdRAg3xQ1M6sOch+6mVkVcJeLmVm1aPo8LeXigG5mVoJHuZiZVQu30M3MqkB4lIuZWfWojHjugG5mVoqHLZqZVQsHdDOzKhBAXbkrkY/XFDUzK0IEinxbybKkwZIelPScpGclnZvSe0u6T9KL6WevlC5JV0qaJ2mOpP2Lle+AbmZWSl1dvq20zcC/R8RewBjgLEl7AecBMyNiJDAzHQMcDYxM22SyJTAb5YBuZlZMfZdLnq1UURFLIuLJtP8W8DwwEDiBbK1R0s/xaf8EYFpkHgd6ShrQWPnuQzczK6EJo1z6SppVcDwlIqY0WKY0lGx90SeA/hGxJJ16Heif9gcCCwsuW5TSltAAB3Qzs1LyB/TlETG6VCZJ3YHfAl+JiDclFbxUhLRtkw24y8XMrKg0OVeeLQdJHciC+a8j4ncp+Y36rpT0c2lKXwwMLrh8UEprkAO6mVkxAdRGvq0EZU3x64DnI+KyglMzgIlpfyJwW0H6hDTaZQywpqBr5j3c5WJmVkIzPil6KPCvwDOSnkpp3wYuBm6SNAlYAJyczt0FHAPMA9YBpxcr3AHdzKyUZgroEfFHslXtGjKugfwBnJW3fAd0M7NiAqjzo/9mZlXAKxaZmVUPB3QzsyoQQG1lzM7lgG5mVlRAOKCbmVUHd7mYmVUBj3IxM6sibqGbmVUJB3QzsyoQAbW15a5FLg7oZmaluIVuZlYlHNDNzKpBeJSLmVlVCAg/WGRmViUq5NF/r1hkZlZMBNTV5dtKkPRzSUslzS1I6y3pPkkvpp+9UrokXSlpnqQ5kvYvVb4DuplZKc23pugvgaO2SjsPmBkRI4GZ6RjgaGBk2iYD15Qq3AHdzKyEqKvLtZUsJ+JhYOVWyScAU9P+VGB8Qfq0yDwO9KxfSLoxDuhmZkXlbJ1v+9DG/gULP78O9E/7A4GFBfkWpbRG+aaomVkxTZucq6+kWQXHUyJiSu6XighJ2/zJ4IBuZlZEAJH/0f/lETG6iS/xhqQBEbEkdaksTemLgcEF+QaltEa5y8XMrJhIC1zk2bbNDGBi2p8I3FaQPiGNdhkDrCnommmQW+hmZiVEMz0pKuk3wFiyrplFwAXAxcBNkiYBC4CTU/a7gGOAecA64PRS5Tugm5mV0kxPikbEZxo5Na6BvAGc1ZTyFRUy6Uw1krSM7BO52vQFlpe7EtYk1fo3e19E9NueAiTdQ/b7yWN5RGw9znyHcUC3Zidp1jbcGLIy8t+sOvimqJlZlXBANzOrEg7o1hJyP0hhrYb/ZlXAfehmZlXCLXQzsyrhgG5mViUc0KuQpFpJT0l6VtLTkv5dUk06N1rSlY1c94qkvONti73+eEl7NXLuQklf397XsPwkrW1C3n6SnpD0V0mHSTqzJetmzcsBvTqtj4hREfFB4KNkE+VfABARsyLinBZ+/fFAgwHdWr1xwDMRsR/Z1K0O6BXEAb3KRcRSstVOvpwm+Rkr6Q4ASX0k3Zta8tcCaqgMSWslfT+19h+X1D+lD5X0QFoea6akIZIOAT4BXJq+JYxorG6SRki6R9JsSY9I2jOlH1/QSrxfUn9JNekbRM+C619M5/pJ+q2kv6Tt0Ob6/VWrhn73kkYBPwBOkPQUcAkwIv0dLy1nfS2niPBWZRuwtoG01WQT548F7khpVwLnp/1jyWYK7dvAtQEcn/Z/AHwn7d8OTEz7/wb8Pu3/EjixkbpdCHw97c8ERqb9g4AH0n4vtozA+jzw32n/CuD0gvz3p/0bgA+n/SHA8+X+G7SmrZF/D4397j8H/DjtDwXmlrv+3vJvnpyrbTsc+BRARNwpaVUj+TYCd6T92WTdOAAH118PXE8W7HOR1B04BLhZeveLQaf0cxAwPc0N3RGYn9KnA+cDvwBOSccAHwH2KihnZ0ndIyJ333FbUuJ3bxXMAb0NkDQcqCWbOP8D21DEpkhNtlROc/y7qQFWR8SoBs5dBVwWETMkjSVr1QM8BuwuqR9ZP/33CsoaExHvNEO92oJiv3urYO5Dr3Ip+P2U7Gv01k+RPQycmvIdTdbV0RR/ImspA5wGPJL23wJ2KnZhRLwJzJd0Unp9Sdo3ne7BlpVZJhZcE8CtwGVk3Sor0ql7gbPr86W+YGtEid99oZJ/R2tdHNCrU5f6YYvA/WQB77sN5PsucHjK9yng1Sa+ztnA6ZLmAP8KnJvSbwS+kW5qNnpTlOxDYJKkp4FnyVY5h6xFfrOk2bx3StfpwGfZ0t0CcA4wOt2cfQ44o4nvo9p1lbSoYPsajf/u35U+MB+VNNc3RSuDH/03M6sSbqGbmVUJB3QzsyrhgG5mViUc0M3MqoQDuplZlXBAt1arYNbIuZJultR1O8r6paQT0/61jc0Gmc6PTXPSNPU1GpytMs8slk2ZETHl96yV9h4O6Naa1c8auTfZ9AP/ML5c0jY9sRoRn4+I54pkGUv2aLxZRXFAt0rxCNlj/2PT7IAzgOcktZN0aZplcY6kL8K7Tz/+WNLfJd0P7FJfkKSHJI1O+0dJejLNJDlT0lCyD46vpm8HhzU2m6NyzlZZSNLv0wyHz0qavNW5y1P6zPSEb6MzUpo1xHO5WKuXWuJHA/ekpP2BvSNifgqKayLiQ5I6kT3ZeC+wH7AH2bzs/YHngJ9vVW4/4GfA4ams3hGxUtJPyWYo/GHKdwNweUT8UdIQ4A9kc+JcAPwxIi6SdCwwKcfb+bf0Gl2Av0j6bXoisxswKyK+Kun8VPaXyRZvPiMiXpR0EHA1cOQ2/BqtDXBAt9asi7J5uSFroV9H1hXy54ion4HxY8A+9f3jZPPAjCSbSfI3EVELvCbpgQbKHwM8XF9WRKxspB4NzuZI/tkqC50j6ZNpf3Cq6wqgji3TGfwK+J1nRbSmckC31mz91jMCpsD2dmEScHZE/GGrfMc0Yz0anM2xIMjmomzmyI8AB0fEOkkPAZ0byR54VkRrIvehW6X7A/AlSR0AJL1fUjeymSQ/nfrYBwBHNHDt42STkw1L1/ZO6VvPMtjYbI5Nna2yB7AqBfM9yb4h1KsB6r9lnErWlZN3VkQzwAHdKt+1ZP3jT0qaC/wP2TfPW4EX07lpZHOp/4OIWEa2PN/v0qyD9V0etwOfrL8pSuOzOTZ1tsp7gPaSngcuJvtAqfc2cGB6D0cCF6X0krMimtXzbItmZlXCLXQzsyrhgG5mViUc0M3MqoQDuplZlXBANzOrEg7oZmZVwgHdzKxK/H8eNsFR2/KeaQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that the optimized XGBoost model is a lot better at identifying people that left the company. Of the **467** people that left the company, **388 (83%)** were correctly classified. However, this improvement was at the expense of not being able to correctly classify as many people that did not leave. That said, this trade off may be better for the company because now it can focus resources on the people that leave it that will help them retain them."
      ],
      "metadata": {
        "id": "ZrqawcuLr-gI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf_xgb = xgb.XGBClassifier(seed=42,\n",
        "                            objective=\"binary:logistic\",\n",
        "                            gamma=1.0,\n",
        "                            learning_rate=0.05,\n",
        "                            max_depth=5,\n",
        "                            reg_lambda=10,\n",
        "                            scale_pos_weight=3,\n",
        "                            subsample=0.9,\n",
        "                            colsample_bytree=0.5,\n",
        "                            eval_metric=\"aucpr\",\n",
        "                            n_estimators=1)\n",
        "clf_xgb.fit(X_train,\n",
        "            y_train)\n",
        "\n",
        "bst = clf_xgb.get_booster()\n",
        "for importance_type in (\"weight\", \"gain\", \"cover\", \"total_gain\", \"total_cover\"):\n",
        "    print(f\"{importance_type}: {bst.get_score(importance_type=importance_type)}\")\n",
        "\n",
        "node_params = {\"shape\": \"box\",\n",
        "               \"style\": \"filled, rounded\",\n",
        "               \"fillcolor\": \"#78cbe\"}\n",
        "\n",
        "leaf_params = {\"shape\": \"box\",\n",
        "               \"style\": \"filled\",\n",
        "               \"fillcolor\": \"#e48038\"}\n",
        "\n",
        "\n",
        "xgb.to_graphviz(clf_xgb, num_trees=0, size=\"10, 10\",\n",
        "                condition_node_params=node_params,\n",
        "                leaf_node_params=leaf_params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 769
        },
        "id": "J_rjHObzycAI",
        "outputId": "9d80cba0-1f51-424a-879f-e5512c23cea3"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "weight: {'passthrough__Zip_Code': 1.0, 'passthrough__Latitude': 1.0, 'passthrough__Longitude': 2.0, 'passthrough__Total_Charges': 9.0, 'onehotencoder__City_Olivehurst': 1.0, 'onehotencoder__Senior_Citizen_No': 1.0, 'onehotencoder__Partner_No': 1.0, 'onehotencoder__Phone_Service_No': 2.0, 'onehotencoder__Internet_Service_No': 1.0, 'onehotencoder__Online_Security_No': 1.0, 'onehotencoder__Online_Backup_No': 1.0, 'onehotencoder__Device_Protection_No': 1.0, 'onehotencoder__Tech_Support_Yes': 3.0, 'onehotencoder__Streaming_TV_No': 2.0, 'onehotencoder__Payment_Method_Credit_card_(automatic)': 1.0, 'onehotencoder__Payment_Method_Mailed_check': 2.0}\n",
            "gain: {'passthrough__Zip_Code': 10.309720039367676, 'passthrough__Latitude': 4.738397598266602, 'passthrough__Longitude': 7.948467254638672, 'passthrough__Total_Charges': 54.91706848144531, 'onehotencoder__City_Olivehurst': 5.74346923828125, 'onehotencoder__Senior_Citizen_No': 20.408409118652344, 'onehotencoder__Partner_No': 12.574092864990234, 'onehotencoder__Phone_Service_No': 8.900158882141113, 'onehotencoder__Internet_Service_No': 30.421142578125, 'onehotencoder__Online_Security_No': 1189.2420654296875, 'onehotencoder__Online_Backup_No': 105.1788330078125, 'onehotencoder__Device_Protection_No': 8.229458808898926, 'onehotencoder__Tech_Support_Yes': 42.18313217163086, 'onehotencoder__Streaming_TV_No': 12.615936279296875, 'onehotencoder__Payment_Method_Credit_card_(automatic)': 15.34429931640625, 'onehotencoder__Payment_Method_Mailed_check': 6.618813514709473}\n",
            "cover: {'passthrough__Zip_Code': 72.5, 'passthrough__Latitude': 57.5, 'passthrough__Longitude': 286.5, 'passthrough__Total_Charges': 273.75, 'onehotencoder__City_Olivehurst': 241.25, 'onehotencoder__Senior_Citizen_No': 136.5, 'onehotencoder__Partner_No': 161.5, 'onehotencoder__Phone_Service_No': 56.625, 'onehotencoder__Internet_Service_No': 468.25, 'onehotencoder__Online_Security_No': 1820.25, 'onehotencoder__Online_Backup_No': 729.75, 'onehotencoder__Device_Protection_No': 54.75, 'onehotencoder__Tech_Support_Yes': 371.6666564941406, 'onehotencoder__Streaming_TV_No': 221.875, 'onehotencoder__Payment_Method_Credit_card_(automatic)': 525.25, 'onehotencoder__Payment_Method_Mailed_check': 57.375}\n",
            "total_gain: {'passthrough__Zip_Code': 10.309720039367676, 'passthrough__Latitude': 4.738397598266602, 'passthrough__Longitude': 15.896934509277344, 'passthrough__Total_Charges': 494.2536315917969, 'onehotencoder__City_Olivehurst': 5.74346923828125, 'onehotencoder__Senior_Citizen_No': 20.408409118652344, 'onehotencoder__Partner_No': 12.574092864990234, 'onehotencoder__Phone_Service_No': 17.800317764282227, 'onehotencoder__Internet_Service_No': 30.421142578125, 'onehotencoder__Online_Security_No': 1189.2420654296875, 'onehotencoder__Online_Backup_No': 105.1788330078125, 'onehotencoder__Device_Protection_No': 8.229458808898926, 'onehotencoder__Tech_Support_Yes': 126.54940032958984, 'onehotencoder__Streaming_TV_No': 25.23187255859375, 'onehotencoder__Payment_Method_Credit_card_(automatic)': 15.34429931640625, 'onehotencoder__Payment_Method_Mailed_check': 13.237627029418945}\n",
            "total_cover: {'passthrough__Zip_Code': 72.5, 'passthrough__Latitude': 57.5, 'passthrough__Longitude': 573.0, 'passthrough__Total_Charges': 2463.75, 'onehotencoder__City_Olivehurst': 241.25, 'onehotencoder__Senior_Citizen_No': 136.5, 'onehotencoder__Partner_No': 161.5, 'onehotencoder__Phone_Service_No': 113.25, 'onehotencoder__Internet_Service_No': 468.25, 'onehotencoder__Online_Security_No': 1820.25, 'onehotencoder__Online_Backup_No': 729.75, 'onehotencoder__Device_Protection_No': 54.75, 'onehotencoder__Tech_Support_Yes': 1115.0, 'onehotencoder__Streaming_TV_No': 443.75, 'onehotencoder__Payment_Method_Credit_card_(automatic)': 525.25, 'onehotencoder__Payment_Method_Mailed_check': 114.75}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f9418133ac0>"
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fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1659.34,-382.04 1648.93,-384.02 1658.3,-388.96 1659.34,-382.04\"/>\n<text text-anchor=\"middle\" x=\"1888.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 2 -->\n<g id=\"node3\" class=\"node\">\n<title>2</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M2829.5,-384C2829.5,-384 2606.5,-384 2606.5,-384 2600.5,-384 2594.5,-378 2594.5,-372 2594.5,-372 2594.5,-360 2594.5,-360 2594.5,-354 2600.5,-348 2606.5,-348 2606.5,-348 2829.5,-348 2829.5,-348 2835.5,-348 2841.5,-354 2841.5,-360 2841.5,-360 2841.5,-372 2841.5,-372 2841.5,-378 2835.5,-384 2829.5,-384\"/>\n<text text-anchor=\"middle\" x=\"2718\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">passthrough__Total_Charges&lt;1181.34998</text>\n</g>\n<!-- 0&#45;&gt;2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>0&#45;&gt;2</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M2227.14,-434.95C2330.75,-420.61 2478.59,-400.14 2584.9,-385.43\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"2585.63,-388.86 2595.06,-384.02 2584.67,-381.92 2585.63,-388.86\"/>\n<text text-anchor=\"middle\" x=\"2455\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 3 -->\n<g id=\"node4\" class=\"node\">\n<title>3</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M792.5,-297C792.5,-297 569.5,-297 569.5,-297 563.5,-297 557.5,-291 557.5,-285 557.5,-285 557.5,-273 557.5,-273 557.5,-267 563.5,-261 569.5,-261 569.5,-261 792.5,-261 792.5,-261 798.5,-261 804.5,-267 804.5,-273 804.5,-273 804.5,-285 804.5,-285 804.5,-291 798.5,-297 792.5,-297\"/>\n<text text-anchor=\"middle\" x=\"681\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">passthrough__Total_Charges&lt;33.4000015</text>\n</g>\n<!-- 1&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>1&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M1413.1,-352.87C1254.65,-337.1 978.9,-309.65 814.82,-293.32\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"814.96,-289.82 804.66,-292.31 814.26,-296.78 814.96,-289.82\"/>\n<text text-anchor=\"middle\" x=\"1194.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M1643,-297C1643,-297 1427,-297 1427,-297 1421,-297 1415,-291 1415,-285 1415,-285 1415,-273 1415,-273 1415,-267 1421,-261 1427,-261 1427,-261 1643,-261 1643,-261 1649,-261 1655,-267 1655,-273 1655,-273 1655,-285 1655,-285 1655,-291 1649,-297 1643,-297\"/>\n<text text-anchor=\"middle\" x=\"1535\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">passthrough__Total_Charges&lt;1225.3501</text>\n</g>\n<!-- 1&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>1&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M1535,-347.8C1535,-336.16 1535,-320.55 1535,-307.24\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1538.5,-307.18 1535,-297.18 1531.5,-307.18 1538.5,-307.18\"/>\n<text text-anchor=\"middle\" x=\"1542\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 5 -->\n<g id=\"node32\" class=\"node\">\n<title>5</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M2825,-297C2825,-297 2611,-297 2611,-297 2605,-297 2599,-291 2599,-285 2599,-285 2599,-273 2599,-273 2599,-267 2605,-261 2611,-261 2611,-261 2825,-261 2825,-261 2831,-261 2837,-267 2837,-273 2837,-273 2837,-285 2837,-285 2837,-291 2831,-297 2825,-297\"/>\n<text text-anchor=\"middle\" x=\"2718\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">onehotencoder__Tech_Support_Yes&lt;0.5</text>\n</g>\n<!-- 2&#45;&gt;5 -->\n<g id=\"edge31\" class=\"edge\">\n<title>2&#45;&gt;5</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M2718,-347.8C2718,-336.16 2718,-320.55 2718,-307.24\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"2721.5,-307.18 2718,-297.18 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text-anchor=\"middle\" x=\"550.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 8 -->\n<g id=\"node7\" class=\"node\">\n<title>8</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M793,-210C793,-210 569,-210 569,-210 563,-210 557,-204 557,-198 557,-198 557,-186 557,-186 557,-180 563,-174 569,-174 569,-174 793,-174 793,-174 799,-174 805,-180 805,-186 805,-186 805,-198 805,-198 805,-204 799,-210 793,-210\"/>\n<text text-anchor=\"middle\" x=\"681\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">onehotencoder__Internet_Service_No&lt;0.5</text>\n</g>\n<!-- 3&#45;&gt;8 -->\n<g id=\"edge6\" class=\"edge\">\n<title>3&#45;&gt;8</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M681,-260.8C681,-249.16 681,-233.55 681,-220.24\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"684.5,-220.18 681,-210.18 677.5,-220.18 684.5,-220.18\"/>\n<text text-anchor=\"middle\" x=\"688\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 9 -->\n<g id=\"node18\" class=\"node\">\n<title>9</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M1536.5,-210C1536.5,-210 1243.5,-210 1243.5,-210 1237.5,-210 1231.5,-204 1231.5,-198 1231.5,-198 1231.5,-186 1231.5,-186 1231.5,-180 1237.5,-174 1243.5,-174 1243.5,-174 1536.5,-174 1536.5,-174 1542.5,-174 1548.5,-180 1548.5,-186 1548.5,-186 1548.5,-198 1548.5,-198 1548.5,-204 1542.5,-210 1536.5,-210\"/>\n<text text-anchor=\"middle\" x=\"1390\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">onehotencoder__Payment_Method_Mailed_check&lt;0.5</text>\n</g>\n<!-- 4&#45;&gt;9 -->\n<g id=\"edge17\" class=\"edge\">\n<title>4&#45;&gt;9</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M1505.66,-260.8C1483.35,-247.72 1452.47,-229.62 1428.23,-215.41\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1429.7,-212.21 1419.3,-210.18 1426.16,-218.25 1429.7,-212.21\"/>\n<text text-anchor=\"middle\" x=\"1506.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 10 -->\n<g id=\"node19\" class=\"node\">\n<title>10</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M1793.5,-210C1793.5,-210 1578.5,-210 1578.5,-210 1572.5,-210 1566.5,-204 1566.5,-198 1566.5,-198 1566.5,-186 1566.5,-186 1566.5,-180 1572.5,-174 1578.5,-174 1578.5,-174 1793.5,-174 1793.5,-174 1799.5,-174 1805.5,-180 1805.5,-186 1805.5,-186 1805.5,-198 1805.5,-198 1805.5,-204 1799.5,-210 1793.5,-210\"/>\n<text text-anchor=\"middle\" x=\"1686\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">onehotencoder__Senior_Citizen_No&lt;0.5</text>\n</g>\n<!-- 4&#45;&gt;10 -->\n<g id=\"edge18\" class=\"edge\">\n<title>4&#45;&gt;10</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M1565.56,-260.8C1588.97,-247.62 1621.44,-229.34 1646.77,-215.08\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1648.75,-217.99 1655.75,-210.03 1645.32,-211.89 1648.75,-217.99\"/>\n<text text-anchor=\"middle\" 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text-anchor=\"middle\" x=\"315\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 35 -->\n<g id=\"node14\" class=\"node\">\n<title>35</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"527,-36 393,-36 393,0 527,0 527,-36\"/>\n<text text-anchor=\"middle\" x=\"460\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=&#45;0.00887096766</text>\n</g>\n<!-- 17&#45;&gt;35 -->\n<g id=\"edge13\" class=\"edge\">\n<title>17&#45;&gt;35</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M563.06,-86.92C551.85,-81.58 539.75,-75.4 529,-69 515.6,-61.02 501.54,-51.14 489.63,-42.27\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"491.47,-39.27 481.38,-36.03 487.24,-44.85 491.47,-39.27\"/>\n<text text-anchor=\"middle\" x=\"563.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 36 -->\n<g id=\"node15\" class=\"node\">\n<title>36</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"659,-36 545,-36 545,0 659,0 659,-36\"/>\n<text text-anchor=\"middle\" x=\"602\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=&#45;0.05810811</text>\n</g>\n<!-- 17&#45;&gt;36 -->\n<g id=\"edge14\" class=\"edge\">\n<title>17&#45;&gt;36</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M602,-86.8C602,-75.16 602,-59.55 602,-46.24\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"605.5,-46.18 602,-36.18 598.5,-46.18 605.5,-46.18\"/>\n<text text-anchor=\"middle\" x=\"609\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 37 -->\n<g id=\"node16\" class=\"node\">\n<title>37</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"805,-36 677,-36 677,0 805,0 805,-36\"/>\n<text text-anchor=\"middle\" x=\"741\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=&#45;0.0792207792</text>\n</g>\n<!-- 18&#45;&gt;37 -->\n<g id=\"edge15\" class=\"edge\">\n<title>18&#45;&gt;37</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M821.24,-86.84C812.55,-81.45 803.2,-75.27 795,-69 784.69,-61.12 774.06,-51.62 764.99,-43.03\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"767.41,-40.5 757.78,-36.08 762.55,-45.54 767.41,-40.5\"/>\n<text text-anchor=\"middle\" x=\"829.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 38 -->\n<g id=\"node17\" class=\"node\">\n<title>38</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"953,-36 823,-36 823,0 953,0 953,-36\"/>\n<text text-anchor=\"middle\" x=\"888\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=0.00454545487</text>\n</g>\n<!-- 18&#45;&gt;38 -->\n<g id=\"edge16\" class=\"edge\">\n<title>18&#45;&gt;38</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M858.49,-86.8C863.65,-74.93 870.62,-58.93 876.49,-45.45\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"879.74,-46.74 880.52,-36.18 873.32,-43.95 879.74,-46.74\"/>\n<text text-anchor=\"middle\" x=\"879\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 19 -->\n<g id=\"node20\" class=\"node\">\n<title>19</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M1262.5,-123C1262.5,-123 1027.5,-123 1027.5,-123 1021.5,-123 1015.5,-117 1015.5,-111 1015.5,-111 1015.5,-99 1015.5,-99 1015.5,-93 1021.5,-87 1027.5,-87 1027.5,-87 1262.5,-87 1262.5,-87 1268.5,-87 1274.5,-93 1274.5,-99 1274.5,-99 1274.5,-111 1274.5,-111 1274.5,-117 1268.5,-123 1262.5,-123\"/>\n<text text-anchor=\"middle\" x=\"1145\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">onehotencoder__Device_Protection_No&lt;0.5</text>\n</g>\n<!-- 9&#45;&gt;19 -->\n<g id=\"edge19\" class=\"edge\">\n<title>9&#45;&gt;19</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M1340.72,-173.9C1301.18,-160.18 1245.56,-140.89 1203.72,-126.37\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1204.8,-123.04 1194.21,-123.07 1202.51,-129.66 1204.8,-123.04\"/>\n<text text-anchor=\"middle\" x=\"1317.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 20 -->\n<g id=\"node21\" class=\"node\">\n<title>20</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M1527.5,-123C1527.5,-123 1304.5,-123 1304.5,-123 1298.5,-123 1292.5,-117 1292.5,-111 1292.5,-111 1292.5,-99 1292.5,-99 1292.5,-93 1298.5,-87 1304.5,-87 1304.5,-87 1527.5,-87 1527.5,-87 1533.5,-87 1539.5,-93 1539.5,-99 1539.5,-99 1539.5,-111 1539.5,-111 1539.5,-117 1533.5,-123 1527.5,-123\"/>\n<text text-anchor=\"middle\" x=\"1416\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">passthrough__Total_Charges&lt;543.824951</text>\n</g>\n<!-- 9&#45;&gt;20 -->\n<g id=\"edge20\" class=\"edge\">\n<title>9&#45;&gt;20</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M1395.26,-173.8C1398.86,-162.05 1403.69,-146.24 1407.79,-132.84\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1411.17,-133.76 1410.75,-123.18 1404.47,-131.71 1411.17,-133.76\"/>\n<text text-anchor=\"middle\" x=\"1412\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 21 -->\n<g id=\"node26\" class=\"node\">\n<title>21</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M1787,-123C1787,-123 1573,-123 1573,-123 1567,-123 1561,-117 1561,-111 1561,-111 1561,-99 1561,-99 1561,-93 1567,-87 1573,-87 1573,-87 1787,-87 1787,-87 1793,-87 1799,-93 1799,-99 1799,-99 1799,-111 1799,-111 1799,-117 1793,-123 1787,-123\"/>\n<text text-anchor=\"middle\" x=\"1680\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">onehotencoder__Tech_Support_Yes&lt;0.5</text>\n</g>\n<!-- 10&#45;&gt;21 -->\n<g id=\"edge25\" class=\"edge\">\n<title>10&#45;&gt;21</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M1684.79,-173.8C1683.96,-162.16 1682.86,-146.55 1681.92,-133.24\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1685.41,-132.9 1681.21,-123.18 1678.43,-133.4 1685.41,-132.9\"/>\n<text text-anchor=\"middle\" x=\"1718.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 22 -->\n<g id=\"node27\" class=\"node\">\n<title>22</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M2044.5,-123C2044.5,-123 1829.5,-123 1829.5,-123 1823.5,-123 1817.5,-117 1817.5,-111 1817.5,-111 1817.5,-99 1817.5,-99 1817.5,-93 1823.5,-87 1829.5,-87 1829.5,-87 2044.5,-87 2044.5,-87 2050.5,-87 2056.5,-93 2056.5,-99 2056.5,-99 2056.5,-111 2056.5,-111 2056.5,-117 2050.5,-123 2044.5,-123\"/>\n<text text-anchor=\"middle\" x=\"1937\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">onehotencoder__Streaming_TV_No&lt;0.5</text>\n</g>\n<!-- 10&#45;&gt;22 -->\n<g id=\"edge26\" class=\"edge\">\n<title>10&#45;&gt;22</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M1736.49,-173.9C1777.08,-160.15 1834.21,-140.81 1877.11,-126.28\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1878.24,-129.59 1886.59,-123.07 1875.99,-122.96 1878.24,-129.59\"/>\n<text text-anchor=\"middle\" x=\"1834\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 39 -->\n<g id=\"node22\" class=\"node\">\n<title>39</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"1099,-36 971,-36 971,0 1099,0 1099,-36\"/>\n<text text-anchor=\"middle\" x=\"1035\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=&#45;0.0134328371</text>\n</g>\n<!-- 19&#45;&gt;39 -->\n<g id=\"edge21\" class=\"edge\">\n<title>19&#45;&gt;39</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M1113.76,-86.88C1104.85,-81.54 1095.3,-75.37 1087,-69 1077.01,-61.34 1066.84,-51.98 1058.18,-43.44\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1060.53,-40.84 1051,-36.21 1055.57,-45.77 1060.53,-40.84\"/>\n<text text-anchor=\"middle\" x=\"1121.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 40 -->\n<g id=\"node23\" class=\"node\">\n<title>40</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"1240.5,-36 1117.5,-36 1117.5,0 1240.5,0 1240.5,-36\"/>\n<text text-anchor=\"middle\" x=\"1179\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=0.0310344826</text>\n</g>\n<!-- 19&#45;&gt;40 -->\n<g id=\"edge22\" class=\"edge\">\n<title>19&#45;&gt;40</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M1151.88,-86.8C1156.58,-75.05 1162.9,-59.24 1168.26,-45.84\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1171.67,-46.76 1172.13,-36.18 1165.17,-44.16 1171.67,-46.76\"/>\n<text text-anchor=\"middle\" x=\"1172\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 41 -->\n<g id=\"node24\" class=\"node\">\n<title>41</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"1389,-36 1259,-36 1259,0 1389,0 1389,-36\"/>\n<text text-anchor=\"middle\" x=\"1324\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=0.00495049497</text>\n</g>\n<!-- 20&#45;&gt;41 -->\n<g id=\"edge23\" class=\"edge\">\n<title>20&#45;&gt;41</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M1390.28,-86.83C1382.83,-81.44 1374.86,-75.26 1368,-69 1359.58,-61.32 1351.13,-52.15 1343.91,-43.77\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1346.53,-41.45 1337.41,-36.05 1341.18,-45.95 1346.53,-41.45\"/>\n<text text-anchor=\"middle\" x=\"1402.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 42 -->\n<g id=\"node25\" class=\"node\">\n<title>42</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"1535,-36 1407,-36 1407,0 1535,0 1535,-36\"/>\n<text text-anchor=\"middle\" x=\"1471\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=&#45;0.0277777798</text>\n</g>\n<!-- 20&#45;&gt;42 -->\n<g id=\"edge24\" class=\"edge\">\n<title>20&#45;&gt;42</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M1427.13,-86.8C1434.96,-74.7 1445.57,-58.3 1454.39,-44.67\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1457.39,-46.47 1459.89,-36.18 1451.52,-42.67 1457.39,-46.47\"/>\n<text text-anchor=\"middle\" x=\"1454\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 43 -->\n<g id=\"node28\" class=\"node\">\n<title>43</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"1676.5,-36 1553.5,-36 1553.5,0 1676.5,0 1676.5,-36\"/>\n<text text-anchor=\"middle\" x=\"1615\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=0.0181818195</text>\n</g>\n<!-- 21&#45;&gt;43 -->\n<g id=\"edge27\" class=\"edge\">\n<title>21&#45;&gt;43</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M1666.85,-86.8C1657.51,-74.59 1644.82,-57.99 1634.33,-44.28\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1636.99,-41.99 1628.13,-36.18 1631.43,-46.25 1636.99,-41.99\"/>\n<text text-anchor=\"middle\" x=\"1686.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 44 -->\n<g id=\"node29\" class=\"node\">\n<title>44</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"1823,-36 1695,-36 1695,0 1823,0 1823,-36\"/>\n<text text-anchor=\"middle\" x=\"1759\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=&#45;0.0147058833</text>\n</g>\n<!-- 21&#45;&gt;44 -->\n<g id=\"edge28\" class=\"edge\">\n<title>21&#45;&gt;44</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M1705,-86.77C1711.86,-81.47 1719.04,-75.35 1725,-69 1731.86,-61.69 1738.33,-52.88 1743.75,-44.7\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1746.76,-46.49 1749.18,-36.18 1740.85,-42.73 1746.76,-46.49\"/>\n<text text-anchor=\"middle\" x=\"1744\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 45 -->\n<g id=\"node30\" class=\"node\">\n<title>45</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"1962.5,-36 1841.5,-36 1841.5,0 1962.5,0 1962.5,-36\"/>\n<text text-anchor=\"middle\" x=\"1902\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=&#45;0.023510972</text>\n</g>\n<!-- 22&#45;&gt;45 -->\n<g id=\"edge29\" class=\"edge\">\n<title>22&#45;&gt;45</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M1929.92,-86.8C1925.03,-74.93 1918.44,-58.93 1912.89,-45.45\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1916.12,-44.09 1909.07,-36.18 1909.64,-46.75 1916.12,-44.09\"/>\n<text text-anchor=\"middle\" x=\"1956.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 46 -->\n<g id=\"node31\" class=\"node\">\n<title>46</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"2109,-36 1981,-36 1981,0 2109,0 2109,-36\"/>\n<text text-anchor=\"middle\" x=\"2045\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=&#45;0.0578947365</text>\n</g>\n<!-- 22&#45;&gt;46 -->\n<g id=\"edge30\" class=\"edge\">\n<title>22&#45;&gt;46</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M1968.32,-86.99C1977.24,-81.65 1986.76,-75.45 1995,-69 2004.62,-61.47 2014.32,-52.24 2022.58,-43.76\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"2025.36,-45.92 2029.72,-36.26 2020.29,-41.09 2025.36,-45.92\"/>\n<text text-anchor=\"middle\" x=\"2019\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 11 -->\n<g id=\"node34\" class=\"node\">\n<title>11</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M2736.5,-210C2736.5,-210 2387.5,-210 2387.5,-210 2381.5,-210 2375.5,-204 2375.5,-198 2375.5,-198 2375.5,-186 2375.5,-186 2375.5,-180 2381.5,-174 2387.5,-174 2387.5,-174 2736.5,-174 2736.5,-174 2742.5,-174 2748.5,-180 2748.5,-186 2748.5,-186 2748.5,-198 2748.5,-198 2748.5,-204 2742.5,-210 2736.5,-210\"/>\n<text text-anchor=\"middle\" x=\"2562\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">onehotencoder__Payment_Method_Credit_card_(automatic)&lt;0.5</text>\n</g>\n<!-- 5&#45;&gt;11 -->\n<g id=\"edge33\" class=\"edge\">\n<title>5&#45;&gt;11</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M2686.43,-260.8C2662.14,-247.56 2628.4,-229.18 2602.19,-214.9\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"2603.71,-211.74 2593.26,-210.03 2600.36,-217.89 2603.71,-211.74\"/>\n<text text-anchor=\"middle\" x=\"2684.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 12 -->\n<g id=\"node35\" class=\"node\">\n<title>12</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M2993.5,-210C2993.5,-210 2778.5,-210 2778.5,-210 2772.5,-210 2766.5,-204 2766.5,-198 2766.5,-198 2766.5,-186 2766.5,-186 2766.5,-180 2772.5,-174 2778.5,-174 2778.5,-174 2993.5,-174 2993.5,-174 2999.5,-174 3005.5,-180 3005.5,-186 3005.5,-186 3005.5,-198 3005.5,-198 3005.5,-204 2999.5,-210 2993.5,-210\"/>\n<text text-anchor=\"middle\" x=\"2886\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">onehotencoder__Phone_Service_No&lt;0.5</text>\n</g>\n<!-- 5&#45;&gt;12 -->\n<g id=\"edge34\" class=\"edge\">\n<title>5&#45;&gt;12</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M2752,-260.8C2778.27,-247.51 2814.8,-229.02 2843.09,-214.71\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"2845,-217.67 2852.34,-210.03 2841.84,-211.42 2845,-217.67\"/>\n<text text-anchor=\"middle\" x=\"2820\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 13 -->\n<g id=\"node48\" class=\"node\">\n<title>13</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M3640.5,-210C3640.5,-210 3425.5,-210 3425.5,-210 3419.5,-210 3413.5,-204 3413.5,-198 3413.5,-198 3413.5,-186 3413.5,-186 3413.5,-180 3419.5,-174 3425.5,-174 3425.5,-174 3640.5,-174 3640.5,-174 3646.5,-174 3652.5,-180 3652.5,-186 3652.5,-186 3652.5,-198 3652.5,-198 3652.5,-204 3646.5,-210 3640.5,-210\"/>\n<text text-anchor=\"middle\" x=\"3533\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">onehotencoder__Streaming_TV_No&lt;0.5</text>\n</g>\n<!-- 6&#45;&gt;13 -->\n<g id=\"edge47\" class=\"edge\">\n<title>6&#45;&gt;13</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M3533,-260.8C3533,-249.16 3533,-233.55 3533,-220.24\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"3536.5,-220.18 3533,-210.18 3529.5,-220.18 3536.5,-220.18\"/>\n<text text-anchor=\"middle\" x=\"3567.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 14 -->\n<g id=\"node49\" class=\"node\">\n<title>14</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M4025.5,-210C4025.5,-210 3852.5,-210 3852.5,-210 3846.5,-210 3840.5,-204 3840.5,-198 3840.5,-198 3840.5,-186 3840.5,-186 3840.5,-180 3846.5,-174 3852.5,-174 3852.5,-174 4025.5,-174 4025.5,-174 4031.5,-174 4037.5,-180 4037.5,-186 4037.5,-186 4037.5,-198 4037.5,-198 4037.5,-204 4031.5,-210 4025.5,-210\"/>\n<text text-anchor=\"middle\" x=\"3939\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">onehotencoder__Partner_No&lt;0.5</text>\n</g>\n<!-- 6&#45;&gt;14 -->\n<g id=\"edge48\" class=\"edge\">\n<title>6&#45;&gt;14</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M3614.67,-260.9C3681.99,-246.81 3777.42,-226.83 3847.33,-212.19\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"3848.39,-215.55 3857.46,-210.07 3846.95,-208.69 3848.39,-215.55\"/>\n<text text-anchor=\"middle\" x=\"3768\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 23 -->\n<g id=\"node36\" class=\"node\">\n<title>23</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M2412,-123C2412,-123 2208,-123 2208,-123 2202,-123 2196,-117 2196,-111 2196,-111 2196,-99 2196,-99 2196,-93 2202,-87 2208,-87 2208,-87 2412,-87 2412,-87 2418,-87 2424,-93 2424,-99 2424,-99 2424,-111 2424,-111 2424,-117 2418,-123 2412,-123\"/>\n<text text-anchor=\"middle\" x=\"2310\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">passthrough__Longitude&lt;&#45;123.862663</text>\n</g>\n<!-- 11&#45;&gt;23 -->\n<g id=\"edge35\" class=\"edge\">\n<title>11&#45;&gt;23</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M2511.31,-173.9C2470.55,-160.15 2413.2,-140.81 2370.13,-126.28\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"2371.21,-122.95 2360.61,-123.07 2368.97,-129.58 2371.21,-122.95\"/>\n<text text-anchor=\"middle\" x=\"2486.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 24 -->\n<g id=\"node37\" class=\"node\">\n<title>24</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M2669.5,-123C2669.5,-123 2454.5,-123 2454.5,-123 2448.5,-123 2442.5,-117 2442.5,-111 2442.5,-111 2442.5,-99 2442.5,-99 2442.5,-93 2448.5,-87 2454.5,-87 2454.5,-87 2669.5,-87 2669.5,-87 2675.5,-87 2681.5,-93 2681.5,-99 2681.5,-99 2681.5,-111 2681.5,-111 2681.5,-117 2675.5,-123 2669.5,-123\"/>\n<text text-anchor=\"middle\" x=\"2562\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">onehotencoder__Phone_Service_No&lt;0.5</text>\n</g>\n<!-- 11&#45;&gt;24 -->\n<g id=\"edge36\" class=\"edge\">\n<title>11&#45;&gt;24</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M2562,-173.8C2562,-162.16 2562,-146.55 2562,-133.24\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"2565.5,-133.18 2562,-123.18 2558.5,-133.18 2565.5,-133.18\"/>\n<text text-anchor=\"middle\" x=\"2569\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 25 -->\n<g id=\"node42\" class=\"node\">\n<title>25</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M2980.5,-123C2980.5,-123 2791.5,-123 2791.5,-123 2785.5,-123 2779.5,-117 2779.5,-111 2779.5,-111 2779.5,-99 2779.5,-99 2779.5,-93 2785.5,-87 2791.5,-87 2791.5,-87 2980.5,-87 2980.5,-87 2986.5,-87 2992.5,-93 2992.5,-99 2992.5,-99 2992.5,-111 2992.5,-111 2992.5,-117 2986.5,-123 2980.5,-123\"/>\n<text text-anchor=\"middle\" x=\"2886\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">passthrough__Latitude&lt;33.8940506</text>\n</g>\n<!-- 12&#45;&gt;25 -->\n<g id=\"edge41\" class=\"edge\">\n<title>12&#45;&gt;25</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M2886,-173.8C2886,-162.16 2886,-146.55 2886,-133.24\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"2889.5,-133.18 2886,-123.18 2882.5,-133.18 2889.5,-133.18\"/>\n<text text-anchor=\"middle\" x=\"2920.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 26 -->\n<g id=\"node43\" class=\"node\">\n<title>26</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M3245.5,-123C3245.5,-123 3022.5,-123 3022.5,-123 3016.5,-123 3010.5,-117 3010.5,-111 3010.5,-111 3010.5,-99 3010.5,-99 3010.5,-93 3016.5,-87 3022.5,-87 3022.5,-87 3245.5,-87 3245.5,-87 3251.5,-87 3257.5,-93 3257.5,-99 3257.5,-99 3257.5,-111 3257.5,-111 3257.5,-117 3251.5,-123 3245.5,-123\"/>\n<text text-anchor=\"middle\" x=\"3134\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">passthrough__Total_Charges&lt;135.574997</text>\n</g>\n<!-- 12&#45;&gt;26 -->\n<g id=\"edge42\" class=\"edge\">\n<title>12&#45;&gt;26</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M2935.89,-173.9C2975.91,-160.18 3032.21,-140.89 3074.56,-126.37\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"3075.87,-129.62 3084.19,-123.07 3073.6,-123 3075.87,-129.62\"/>\n<text text-anchor=\"middle\" x=\"3032\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 47 -->\n<g id=\"node38\" class=\"node\">\n<title>47</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"2257,-36 2127,-36 2127,0 2257,0 2257,-36\"/>\n<text text-anchor=\"middle\" x=\"2192\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=0.00357142882</text>\n</g>\n<!-- 23&#45;&gt;47 -->\n<g id=\"edge37\" class=\"edge\">\n<title>23&#45;&gt;47</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M2277.59,-87C2268.13,-81.61 2257.94,-75.39 2249,-69 2238.12,-61.23 2226.9,-51.75 2217.33,-43.15\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"2219.45,-40.35 2209.71,-36.18 2214.73,-45.52 2219.45,-40.35\"/>\n<text text-anchor=\"middle\" x=\"2283.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 48 -->\n<g id=\"node39\" class=\"node\">\n<title>48</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"2398.5,-36 2275.5,-36 2275.5,0 2398.5,0 2398.5,-36\"/>\n<text text-anchor=\"middle\" x=\"2337\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=0.0661224499</text>\n</g>\n<!-- 23&#45;&gt;48 -->\n<g id=\"edge38\" class=\"edge\">\n<title>23&#45;&gt;48</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M2315.46,-86.8C2319.2,-75.05 2324.22,-59.24 2328.47,-45.84\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"2331.85,-46.77 2331.54,-36.18 2325.18,-44.65 2331.85,-46.77\"/>\n<text text-anchor=\"middle\" x=\"2333\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 49 -->\n<g id=\"node40\" class=\"node\">\n<title>49</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"2539.5,-36 2416.5,-36 2416.5,0 2539.5,0 2539.5,-36\"/>\n<text text-anchor=\"middle\" x=\"2478\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=0.0360000022</text>\n</g>\n<!-- 24&#45;&gt;49 -->\n<g id=\"edge39\" class=\"edge\">\n<title>24&#45;&gt;49</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M2537.11,-86.99C2529.97,-81.61 2522.38,-75.39 2516,-69 2508.56,-61.56 2501.33,-52.63 2495.21,-44.38\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"2497.96,-42.2 2489.27,-36.13 2492.28,-46.29 2497.96,-42.2\"/>\n<text text-anchor=\"middle\" x=\"2550.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 50 -->\n<g id=\"node41\" class=\"node\">\n<title>50</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"2686,-36 2558,-36 2558,0 2686,0 2686,-36\"/>\n<text text-anchor=\"middle\" x=\"2622\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=&#45;0.0171428584</text>\n</g>\n<!-- 24&#45;&gt;50 -->\n<g id=\"edge40\" class=\"edge\">\n<title>24&#45;&gt;50</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M2574.14,-86.8C2582.68,-74.7 2594.26,-58.3 2603.88,-44.67\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"2606.97,-46.36 2609.88,-36.18 2601.25,-42.33 2606.97,-46.36\"/>\n<text text-anchor=\"middle\" x=\"2603\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 51 -->\n<g id=\"node44\" class=\"node\">\n<title>51</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"2832,-36 2704,-36 2704,0 2832,0 2832,-36\"/>\n<text text-anchor=\"middle\" x=\"2768\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=&#45;0.0012048193</text>\n</g>\n<!-- 25&#45;&gt;51 -->\n<g id=\"edge43\" class=\"edge\">\n<title>25&#45;&gt;51</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M2853.59,-87C2844.13,-81.61 2833.94,-75.39 2825,-69 2814.12,-61.23 2802.9,-51.75 2793.33,-43.15\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"2795.45,-40.35 2785.71,-36.18 2790.73,-45.52 2795.45,-40.35\"/>\n<text text-anchor=\"middle\" x=\"2859.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 52 -->\n<g id=\"node45\" class=\"node\">\n<title>52</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"2973.5,-36 2850.5,-36 2850.5,0 2973.5,0 2973.5,-36\"/>\n<text text-anchor=\"middle\" x=\"2912\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=0.0339207053</text>\n</g>\n<!-- 25&#45;&gt;52 -->\n<g id=\"edge44\" class=\"edge\">\n<title>25&#45;&gt;52</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M2891.26,-86.8C2894.86,-75.05 2899.69,-59.24 2903.79,-45.84\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"2907.17,-46.76 2906.75,-36.18 2900.47,-44.71 2907.17,-46.76\"/>\n<text text-anchor=\"middle\" x=\"2908\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 53 -->\n<g id=\"node46\" class=\"node\">\n<title>53</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"3114.5,-36 2991.5,-36 2991.5,0 3114.5,0 3114.5,-36\"/>\n<text text-anchor=\"middle\" x=\"3053\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=0.0142857153</text>\n</g>\n<!-- 26&#45;&gt;53 -->\n<g id=\"edge45\" class=\"edge\">\n<title>26&#45;&gt;53</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M3109.1,-86.67C3102.25,-81.37 3095.04,-75.27 3089,-69 3081.91,-61.65 3075.11,-52.82 3069.36,-44.65\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"3072.08,-42.44 3063.57,-36.14 3066.29,-46.37 3072.08,-42.44\"/>\n<text text-anchor=\"middle\" x=\"3123.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 54 -->\n<g id=\"node47\" class=\"node\">\n<title>54</title>\n<polygon fill=\"#e48038\" stroke=\"black\" points=\"3261,-36 3133,-36 3133,0 3261,0 3261,-36\"/>\n<text text-anchor=\"middle\" x=\"3197\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=&#45;0.0146341464</text>\n</g>\n<!-- 26&#45;&gt;54 -->\n<g id=\"edge46\" class=\"edge\">\n<title>26&#45;&gt;54</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M3146.75,-86.8C3155.8,-74.59 3168.1,-57.99 3178.26,-44.28\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"3181.13,-46.29 3184.27,-36.18 3175.5,-42.13 3181.13,-46.29\"/>\n<text text-anchor=\"middle\" x=\"3177\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 27 -->\n<g id=\"node50\" class=\"node\">\n<title>27</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M3509.5,-123C3509.5,-123 3300.5,-123 3300.5,-123 3294.5,-123 3288.5,-117 3288.5,-111 3288.5,-111 3288.5,-99 3288.5,-99 3288.5,-93 3294.5,-87 3300.5,-87 3300.5,-87 3509.5,-87 3509.5,-87 3515.5,-87 3521.5,-93 3521.5,-99 3521.5,-99 3521.5,-111 3521.5,-111 3521.5,-117 3515.5,-123 3509.5,-123\"/>\n<text text-anchor=\"middle\" x=\"3405\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">passthrough__Total_Charges&lt;4880.125</text>\n</g>\n<!-- 13&#45;&gt;27 -->\n<g id=\"edge49\" class=\"edge\">\n<title>13&#45;&gt;27</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M3507.1,-173.8C3487.58,-160.84 3460.63,-142.94 3439.32,-128.79\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"3441.13,-125.79 3430.86,-123.18 3437.26,-131.62 3441.13,-125.79\"/>\n<text text-anchor=\"middle\" x=\"3511.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 28 -->\n<g id=\"node51\" class=\"node\">\n<title>28</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M3768,-123C3768,-123 3552,-123 3552,-123 3546,-123 3540,-117 3540,-111 3540,-111 3540,-99 3540,-99 3540,-93 3546,-87 3552,-87 3552,-87 3768,-87 3768,-87 3774,-87 3780,-93 3780,-99 3780,-99 3780,-111 3780,-111 3780,-117 3774,-123 3768,-123\"/>\n<text text-anchor=\"middle\" x=\"3660\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">passthrough__Total_Charges&lt;4551.0498</text>\n</g>\n<!-- 13&#45;&gt;28 -->\n<g id=\"edge50\" class=\"edge\">\n<title>13&#45;&gt;28</title>\n<path fill=\"none\" stroke=\"#ff0000\" d=\"M3558.7,-173.8C3578.07,-160.84 3604.81,-142.94 3625.95,-128.79\"/>\n<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"3627.97,-131.65 3634.34,-123.18 3624.08,-125.83 3627.97,-131.65\"/>\n<text text-anchor=\"middle\" x=\"3612\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n</g>\n<!-- 29 -->\n<g id=\"node56\" class=\"node\">\n<title>29</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M4041,-123C4041,-123 3837,-123 3837,-123 3831,-123 3825,-117 3825,-111 3825,-111 3825,-99 3825,-99 3825,-93 3831,-87 3837,-87 3837,-87 4041,-87 4041,-87 4047,-87 4053,-93 4053,-99 4053,-99 4053,-111 4053,-111 4053,-117 4047,-123 4041,-123\"/>\n<text text-anchor=\"middle\" x=\"3939\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">passthrough__Longitude&lt;&#45;117.251846</text>\n</g>\n<!-- 14&#45;&gt;29 -->\n<g id=\"edge55\" class=\"edge\">\n<title>14&#45;&gt;29</title>\n<path fill=\"none\" stroke=\"#0000ff\" d=\"M3939,-173.8C3939,-162.16 3939,-146.55 3939,-133.24\"/>\n<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"3942.5,-133.18 3939,-123.18 3935.5,-133.18 3942.5,-133.18\"/>\n<text text-anchor=\"middle\" x=\"3973.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n</g>\n<!-- 30 -->\n<g id=\"node57\" class=\"node\">\n<title>30</title>\n<path fill=\"#78cb0e\" stroke=\"black\" d=\"M4290.5,-123C4290.5,-123 4123.5,-123 4123.5,-123 4117.5,-123 4111.5,-117 4111.5,-111 4111.5,-111 4111.5,-99 4111.5,-99 4111.5,-93 4117.5,-87 4123.5,-87 4123.5,-87 4290.5,-87 4290.5,-87 4296.5,-87 4302.5,-93 4302.5,-99 4302.5,-99 4302.5,-111 4302.5,-111 4302.5,-117 4296.5,-123 4290.5,-123\"/>\n<text text-anchor=\"middle\" x=\"4207\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">passthrough__Zip_Code&lt;95589</text>\n</g>\n<!-- 14&#45;&gt;30 -->\n<g id=\"edge56\" 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          },
          "metadata": {},
          "execution_count": 85
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      ]
    }
  ]
}