Churn_Analysis_.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vin1Ux_3ZbBc"
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      "source": [
        "# Churn Customers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RH4zaM_hZbBd"
      },
      "source": [
        "### Introduction\n",
        "This IBM Sample Dataset has information about Telco customers and if they left the company within the last month (churn).\n",
        "\n",
        "Basic information:\n",
        "Customers who left within the last month – the column is called Churn.\n",
        "Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies.\n",
        "Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges.\n",
        "Demographic info about customers – gender, age range, and if they have partners and dependents.\n",
        "There are 21 columns with 19 features.\n",
        "\n",
        "#### Objective\n",
        "I will explore the data and try to answer some questions like:\n",
        "\n",
        "Customer churn measures how and why are customers leaving the business\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FlmRkLKWZbBe"
      },
      "source": [
        "### Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVd6_tzPZbBe"
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBTIV7h4ZbBi"
      },
      "source": [
        "\n",
        "### Loading data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhRD6xeXZbBj",
        "outputId": "d1b736e1-042f-4193-99ed-4a6bd758d1a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "df = pd.read_csv(\"Churndata.csv\")\n",
        "df.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3821</th>\n",
              "      <td>1833-VGRUM</td>\n",
              "      <td>Female</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</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>74.20</td>\n",
              "      <td>74.2</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4831</th>\n",
              "      <td>4654-GGUII</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>18</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>40.20</td>\n",
              "      <td>711.95</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6528</th>\n",
              "      <td>4957-SREEC</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>70</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</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>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>20.35</td>\n",
              "      <td>1458.1</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6126</th>\n",
              "      <td>9190-MFJLN</td>\n",
              "      <td>Male</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>19</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</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>Credit card (automatic)</td>\n",
              "      <td>95.90</td>\n",
              "      <td>1777.9</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4267</th>\n",
              "      <td>1227-UDMZR</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>72</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Two year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>91.15</td>\n",
              "      <td>6637.9</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      customerID  gender  SeniorCitizen  ... MonthlyCharges TotalCharges  Churn\n",
              "3821  1833-VGRUM  Female              1  ...          74.20         74.2    Yes\n",
              "4831  4654-GGUII  Female              0  ...          40.20       711.95     No\n",
              "6528  4957-SREEC    Male              0  ...          20.35       1458.1     No\n",
              "6126  9190-MFJLN    Male              1  ...          95.90       1777.9    Yes\n",
              "4267  1227-UDMZR  Female              0  ...          91.15       6637.9     No\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0y2ZswuWZbBm"
      },
      "source": [
        "### Pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RUyqHnDZbBn",
        "outputId": "ebf6f54c-0d07-4a4c-de60-3a7ef8ce9b1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "#Checking for datatypes\n",
        "#Wesee that Total charges is an object. We need to change that to float\n",
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID           object\n",
              "gender               object\n",
              "SeniorCitizen         int64\n",
              "Partner              object\n",
              "Dependents           object\n",
              "tenure                int64\n",
              "PhoneService         object\n",
              "MultipleLines        object\n",
              "InternetService      object\n",
              "OnlineSecurity       object\n",
              "OnlineBackup         object\n",
              "DeviceProtection     object\n",
              "TechSupport          object\n",
              "StreamingTV          object\n",
              "StreamingMovies      object\n",
              "Contract             object\n",
              "PaperlessBilling     object\n",
              "PaymentMethod        object\n",
              "MonthlyCharges      float64\n",
              "TotalCharges         object\n",
              "Churn                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTl5_pmwZbBq",
        "outputId": "8f164be7-e754-46bc-fa9b-1bcff5066eef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "df.TotalCharges.values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['29.85', '1889.5', '108.15', ..., '346.45', '306.6', '6844.5'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYjcn1VtZbBt"
      },
      "source": [
        "df['TotalCharges'] = df['TotalCharges'].replace(\" \", 0).astype('float64')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-de3RGkZbBw",
        "outputId": "24a5c4c9-97ce-4cf8-e733-15c15c54a553",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID           object\n",
              "gender               object\n",
              "SeniorCitizen         int64\n",
              "Partner              object\n",
              "Dependents           object\n",
              "tenure                int64\n",
              "PhoneService         object\n",
              "MultipleLines        object\n",
              "InternetService      object\n",
              "OnlineSecurity       object\n",
              "OnlineBackup         object\n",
              "DeviceProtection     object\n",
              "TechSupport          object\n",
              "StreamingTV          object\n",
              "StreamingMovies      object\n",
              "Contract             object\n",
              "PaperlessBilling     object\n",
              "PaymentMethod        object\n",
              "MonthlyCharges      float64\n",
              "TotalCharges        float64\n",
              "Churn                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G75J_YNQZbB0"
      },
      "source": [
        "### Data Visualization "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HxnZCaPDZbB0"
      },
      "source": [
        "##### Viz1 : \n",
        "No. of customers Vs. Tenure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvyzlFo6ZbB1",
        "outputId": "5a87f77e-075a-48ef-8672-2cddb579964e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "tenure_churn_no = df[df.Churn=='No'].tenure\n",
        "tenure_churn_yes = df[df.Churn=='Yes'].tenure\n",
        "\n",
        "plt.xlabel(\"tenure\")\n",
        "plt.ylabel(\"Number Of Customers\")\n",
        "plt.title(\"Customer Churn Prediction Visualization\")\n",
        "\n",
        "blood_sugar_men = [113, 85, 90, 150, 149, 88, 93, 115, 135, 80, 77, 82, 129]\n",
        "blood_sugar_women = [67, 98, 89, 120, 133, 150, 84, 69, 89, 79, 120, 112, 100]\n",
        "\n",
        "plt.hist([tenure_churn_yes, tenure_churn_no], rwidth=0.95, color=['green','red'],label=['Churn=Yes','Churn=No'])\n",
        "plt.legend()\n",
        "\n",
        "#In this graph, we have the number of customers who will be leaving and not leaving the company, and we have the tenure of months that they have been apart of the company till now."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f877bf79cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H5_hEt8VZbB7"
      },
      "source": [
        "##### Churn Vs. Count\n",
        "\n",
        "Churn: No - 72.4%\n",
        "\n",
        "Churn: Yes - 27.6%"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7uk0H__AZbCE",
        "outputId": "bf307e96-cbc4-48d7-a6c6-cd30d6ca8825",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        }
      },
      "source": [
        "ax = sns.catplot(y=\"Churn\", kind=\"count\", data=df, height=2.6, aspect=2.5, orient='h')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 468x187.2 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "shn2XmrGZbCH"
      },
      "source": [
        "##### Numeric Features\n",
        "\n",
        "There are only three numerical columns: tenure, monthly charges and total charges. The probability density distribution can be estimate using the seaborn kdeplot function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-X5biEExZbCH",
        "outputId": "b8da427b-d17d-4e01-f155-76c19b60aabe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        }
      },
      "source": [
        "def kdeplot(feature):\n",
        "    plt.figure(figsize=(9, 4))\n",
        "    plt.title(\"KDE for {}\".format(feature))\n",
        "    ax0 = sns.kdeplot(df[df['Churn'] == 'No'][feature].dropna(), color= 'navy', label= 'Churn: No')\n",
        "    ax1 = sns.kdeplot(df[df['Churn'] == 'Yes'][feature].dropna(), color= 'orange', label= 'Churn: Yes')\n",
        "kdeplot('tenure')\n",
        "kdeplot('MonthlyCharges')\n",
        "kdeplot('TotalCharges')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uOb+hLachQ1pSVGRYsCCtagJTypOMgbV/gRqNrcJhXxMUbA3J9f0Irs60prs3HgQZP8CyW+DbWGvBw19ugk3/gQMLoeCY3VErVSUqNt2mAowxhSJyD/ADEAy8b4zZICJPAyuNMdOA94CPRWQbcBgrMQJARNKAKCBMREYDlxljUh2nrwOuKPHIP4rISKDQ0dYET302ZZk/fydhYcH07Vtq+ZTb+vSJJyIilDlztjN6dPsqik4pD9k3E7IWQc83fH/vq9BIa7p7i5usmWhH10HGHOvz7Z8PaZ84LhSIbGPtDl+vu/W1fiLUaGRr+EpVlG44qhuOnrekpLeJiAjj558nVLqtK6/8jC1bDrFly72VD0wpTzHFMKurVQR8ZSoEhdodkWedzITDq6zXkbXW6/jO385HtoGGF0HTYdbmqqGR9sWqFOVvOOqxnh7l344cOcnq1Rk8+eTAKmlvyJCWzJy5lbS0oyQk1K2SNpWqcnunQfZ66Pup/yc8ADWbQOxw6+WUf9Sa+n54BRxYBHu+hh3vQ1AYNB1urUrdZIg1jKaUl/Hl2VvKRgsX7sIYKl3P4zRkiLWa85w526ukPaWqnDGQ+gJEtIBm19kdjX3C6kLjgdDhIRj4HVyTBZf+DG3usobFfhoGM9rDzk+tTViV8iKa9KjzsnDhLmrUCKFXr5JLL52fDh0a0LRpJPPm7Sz/YqXskLUEDi2zNhMN0k7y04JCoNEA6PFvGL0X+n0BIbVg6Y3WukBZS+2OUKnTNOlR5yU5OZ3ExJgKbz1xLiLCgAHNWbx4t25JobxT6gsQHg0tb7U7Eu8VHA7Nr4dha6zkpyDbmhK/+iFdDFF5BU16VIUVFBSxalUGF15YNb08Tv37x5Oefozdu7OrtF2lKi17I+ybAW3vtXoxVNkkyEp+hq+H1nfAppdg7iCrMFopG2nSoyosJeUAp04VVtnQllO/ftbeRYsX767SdpWqtK1vWoW6be6yOxLfEhplrVjdf6o1Hf6HXrr/l7KVJj2qwpKT9wJUeU9Ply6NiIwMY8mSPeVfrFR1KTwOOz+C+DFQQ7euOS/NroEhi60p//Mu0cRH2UaTHlVhycnpNGxYq8qnlgcHB9GnT7z29CjvsmuyVZvS5k67I/Ft9bvDkIXW8OD8S+HoBrsjUgFIkx5VYcnJ6Vx4YRzigU0W+/ePZ/36Axw9qkWPykts/R/U6QgN+9sdie+r3RIGL7DWOFowBE6U3I5RKc/SpEdVyNGjp9i06WCVD2059evXDGNg6VId4lJe4PAqaxG+1nd6907qviSyNQz60drPa9HVOqtLVStNelSFrFxp7cBc1UXMThdeGEtwsGhdj/IO296F4JrW3lSq6tTtDH0+hkPLIXmitfCjUtVAkx5VIc4iZk8lPRERYXTvHqNJj7JfUR7sngxxo61ViFXVih8NXZ6CtI9hx4d2R6MChCY9qkKSk9Np1y6aunVreOwZ/frFk5y8l8LCYo89Q6ly7ZsF+Ue0l8eTOv2ftZrzqvsgN83uaFQA0KRHVciqVRkkJTX16DN6947j5MlCNmw44NHnKFWmnR9DjcbW5pnKM4KCofck6/2yCdaUdqU8SJMe5bYDB46zb98xundv4tHn9OxpJVXLl+vMDmWTvMPWCszNx+k+W55WOwF6vAIHfoYtb9gdjfJzmvQot/36q7WEfPfuMR59TsuW9ahfv6YmPco+u6dAcYEObVWXlhOsHrV1/wcn99sdjfJjmvQot61ZYyU9Xbs29uhzRIRevWJZvnyfR5+j1DmlfWqtzVOvu92RBAYRSPovFJ2AXx+xOxrlxzTpUW5buzaT+PgooqM9v+Fir15NWb/+AMeP53v8WUqd4UQ6ZC22hrZ0bZ7qE9UO2v3JmsmVtdTuaJSf8mjSIyJDRWSziGwTkbPSdxEJF5HJjvPJIpLgOB4tIgtEJFdEXitxz0+ONtc6Xo3KaktVnbVrMz0+tOXUq1csxcXmdO+SUtVmz9fW1/gx9sYRiDo/DjVjYfX9unaP8giPJT0iEgy8DgwDOgLjRKRjictuA44YY1oD/wZecBw/BTwOPHSO5scbY7o5Xs4pPudqS1WBEycK2Lz5EN26eXZoy6lnT2sdIK3rUdVu95dQpzPUaW93JIEntDZc8Ddr0cL0aXZHo/yQJ3t6egHbjDE7jDH5wBfAqBLXjAIc8xWZCgwWETHGHDfGLMZKftxValvnH75ylZKyn+JiQ7dunp255dSoUQTNm9fRpEdVr5MZ1tBWM+3lsU2LWyCyDfz6GBQX2R2N8jOeTHpiAddldfc6jpV6jTGmEMgGot1o+wPH0NbjLonN+bal3LB2bfXM3HJlFTNr0qOq0Z6vAQPNrrU7ksAVFAIX/B2yN8Cuz+2ORvkZXyxkHm+M6QJc5HhVaE6piEwUkZUisjIrK8sjAfqjtWszqVMnnObN61TbM3v1imXnzqNkZR2vtmeqALd7qjVrq07JkXhVrZpdC/W6QcqTUFxodzTKj3gy6UkH4l2+j3McK/UaEQkB6gCHymrUGJPu+HoM+AxrGM3ttowxbxtjkowxSQ0bNqzgRwpca9Zk0q1bE6pzxNC5SKFzk1OlPOrUAchaCPHX2B2JkiDo/CTk7rBqrJSqIp5MelYAbUSkhYiEAWOBkpVp04BbHO/HAPONOXfJvoiEiEgDx/tQ4Epg/fm0pdxXVFTMunX7Pb4Sc0nOobTVqzOq9bkqQKXPtLZBiLvK7kgUQNxIiGoPqc/rTC5VZTy2vroxplBE7gF+AIKB940xG0TkaWClMWYa8B7wsYhsAw5jJUYAiEgaEAWEicho4DJgF/CDI+EJBuYC7zhuOWdbqnK2bz/CyZOFdO1avUlPVFQ4bdrUZ/VqnbauqkH6d1Ar3hpWUfaTIOj4F1h2K2TMhqbD7I5I+QGPbipjjJkFzCpx7AmX96eAUisGjTEJ52i2xzmuP2dbqnJSUqxl4bt0aVTtz05MjCE5WYuZlYcVnoSMH6Hl73RBQm/S/AZY97jV26NJj6oCvljIrKrZ+vUHEIEOHaq/BioxMYa0tKMcPnyy2p+tAkjmXCg6CXElV9VQtgoOg/YPwoGFcHC53dEoP6BJjyrX+vVZtGpVn1q1Qqv92YmJVl3PmjVa16M8KH0ahERCo4F2R6JKanWb9c9my3/tjkT5AU16VLlSUvbbMrQFnC6e1mJm5TGmGNKnW8MnwWF2R6NKCo2ElrfA7im6A7uqNE16VJlOnSpk69bDdO5sT9ITHV2L5s3raDGz8pxDy+HUfh3a8mZt7obifNj+TvnXKlUGTXpUmTZtOkhxsbEt6QFriEt7epTHpM+0ZgrFDLU7EnUuddpDkyGw9X9QXGB3NMqHadKjymTnzC2nxMQYtmw5RE5Onm0xKD+W8T006Avh9e2ORJWl7b1wMh32fmt3JMqHadKjyrR+/QHCwoJp3dq+XwjOYuZff9UhLlXFTmbC4VXQ9Aq7I1HlaXoF1GoG23SIS50/TXpUmdavz6JDhwaEhgbbFoMz6dEhLlXlMmZbX3UNGO8XFAytfmctL5CbZnc0ykdp0qPKlJKy39Z6HoAmTWoTE1Nbi5lV1ds3C2rGQN2udkei3NHyVuvrjg/sjUP5LE161DllZ59iz54c25Me0GJm5QHFBdYqzE2v0FWYfUVEM4i5DHa8D8VFdkejfJAmPeqcNmzIAuwtYnZKTIwhNTWLEyd05oaqIgeXQkG21vP4mla3w4m9kDnH7kiUD9KkR53T+vUHAOjUyTuSnuJic3o2mVKVtm8WSAg0udTuSFRFxI6E8Aaw/V27I1E+SJMedU6pqVlERITSrFkdu0PRYmZV9TJ+hIZ9ITTK7khURQSHWRuRpk+H/CN2R6N8jCY96pxSU7Po0KEhQUH21zvEx0cRHV1Tkx5VNU5lwZE11oJ3yve0uMlaoXn3VLsjUT5Gkx51TqmpWXTsWP07q5dGRBzFzDqDS1WBzHnWV016fFP9HhDVHnZ+bHckysdo0qNKlZOTR3r6MTp2bGB3KKclJsaQkrKf/HydtaEqKfNHCK0L9ZPsjkSdDxFIuBGyFumaPapCNOlRpdq40Zq51aGDd/T0gJX0FBQUs2HDAbtDUb7MGGvmT5PB1oJ3yjcljLe+pn1qbxzKp2jSo0qVmmolPd4yvAXQvXsTQIuZVSXlbLamPOvQlm+rnQCNBkDax1Yiq5QbNOlRpUpNzSI8PJgWLeraHcpprVrVJzIyTJMeVTnO9V1iNOnxeQnjrST2aIrdkSgf4dGkR0SGishmEdkmIo+Ucj5cRCY7zieLSILjeLSILBCRXBF5zeX6WiIyU0Q2icgGEXne5dwEEckSkbWO1+2e/Gz+LjX1IO3bNyA42Hvy4qAgoXv3GFat0qRHVULGj1C7FdRuaXckqrLirgIJht1T7I5E+QiP/UYTkWDgdWAY0BEYJyIdS1x2G3DEGNMa+DfwguP4KeBx4KFSmv6XMaY90B3oJyKuOwVONsZ0c7x05apK2LjRe2ZuuUpMbMK6dfspLCy2OxTli4oL4MBPOrTlL2o0hEYXw+4vdYhLucWTf8b3ArYZY3YYY/KBL4BRJa4ZBUxyvJ8KDBYRMcYcN8Ysxkp+TjPGnDDGLHC8zwdWA3Ee/AwB6fjxfNLSjtKhg/fM3HJKTIzh5MlCNm8+aHcoyhcdXAaFuTq05U+aXQvHtugQl3KLJ5OeWGCPy/d7HcdKvcYYUwhkA9HuNC4idYERwDyXw9eIyDoRmSoi8ee4b6KIrBSRlVlZWe59kgCzefMhjPGuImYnXZlZVUrmHJAgaHyJ3ZGoqhJ/lfXPdPeXdkeifID3FGxUgIiEAJ8DrxpjdjgOTwcSjDEXAHP4rQfpDMaYt40xScaYpIYNve+XujfwxplbTu3aNaBmzRBNetT5yZgD9XtBmPcU6KtKqtHIGuLao0NcqnyeTHrSAdfeljjHsVKvcSQydYBDbrT9NrDVGPMf5wFjzCFjTJ7j23eBHucZd8BLTc0iJCSI1q3r2x3KWUJCgujatYmuzKwqLv8IHF6uQ1v+qNm11iyu7PV2R6K8nCeTnhVAGxFpISJhwFhgWolrpgG3ON6PAeYbU3aqLiL/wEqO7i9xPMbl25HAxkrEHtBSU7No2zaa0FDvXLgtMbEJa9dmUlysf9WpCti/AEyxFjH7o/irdYhLucVjSY+jRuce4AesBGSKMWaDiDwtIiMdl70HRIvINuAB4PS0dhFJA14GJojIXhHpKCJxwGNYs8FWl5ia/kfHNPZfgT8CEzz12fzdxo0HvbKI2SkxMYacnDx27NAdllUFZMyBkNrQoLfdkaiqVqMRNBqos7hUuUI82bgxZhYwq8SxJ1zenwKuPce9CedottQtv40xjwKPnleg6rS8vEK2bTvM9dd3sjuUc3ItZvbGITjlpTLnQONBEBRqdyTKE5pdCyvuguwNULez3dEoL+WThczKc7ZsOURxsfHKImanTp0aERoapMXMyn25OyB3uw5t+bM4HeJS5dOkR53Bm2duOYWFBdO5cyNNepT7MhxbT2jS479qNoaGA3SIS5VJkx51htTULIKChLZt3VouyTaJiTGsXp1BOXXvSlky50CtOIhqZ3ckypOaXQs5G60hLqVKoUmPOsPGjQdp2bIeNWp4tNyr0hITYzh06CR79uTYHYrydsVFkDkPmlwGUmpJoPIX8VcDArun2h2J8lKa9KgzpKZ6555bJenKzMpth1dCwVEd2goENZtAw76Q/p3dkSgvpUmPOq2goIgtWw7RsaP3Tld3uuCCxgQFiSY9qnyZznqewfbGoapH7Cg4shaO77Y7EuWFNOlRp23ffoSCgmKf6OmpVSuUDh0aaNKjypc5B+p1t3bkVv4vzrEM3N6Sa+Eq5eY6PSLyNdZCgt8bY4o9G5Kyiy/M3HKVmBjD3Lk7yr9QBa6CY3BwKbR/wO5Izun48Xy2bTvM1q2H2bHjCEeOnCQnJ4+cnHxycvLIyyskPDyE8PBgwsNDqFEjmAYNatG0aSSxsVE0bRpJfLz1VbRmySpWj2pnDXG1u8fuaJSXcbda9Q3gVuBVEfkS+MAYs9lzYSk7OJOe9u29f3gLrKTn44/XkZmZS5Mmte0OR3mjAz9DcYHX1POcPPHuQZUAACAASURBVFnA8uXpLF68m8WL9/Drr5lkZOSecU1oaBB16tQgKiqcqKhwwsKCyc8v4tSpQvLyCjl1qpCDB09QUHDm35916oTTuXOj06/ExBiSkpoSFuad28l4VOwo2PQy5B/VzWXVGdxKeowxc4G5IlIHGOd4vwd4B/jEGFPgwRhVNdm48SDNm9chIiLM7lDc4ixmXrMmg2HD2tgcjfJKGXMguAY07GdbCFu2HOKrr1KZPn0LK1fuO52sdO7ciMsvb02bNvVp06Y+rVtbr8jI8HLbLC42HDx4gn37jpGenkNa2lFSU7NYvz6LKVM28NZbqwCoUSOEXr1iueiiZvTv34wBA5pTq1YArEgdNwo2/hP2zYaEsXZHo7yI2/OSRSQauBG4CVgDfAr0x9ow9GJPBKeql6/M3HLq1q0JYM3g0qRHlSpzjrVgXXCNan3sxo1W8jF16kbWrz8AQM+eTXnggT7079+Mvn3jqV+/5nm3HxQkNGoUQaNGEaf/O3AyxpCRkUty8l4WL97NokW7ef75xRQVGWrWDGHw4JZceWUbrryyLbGxUZX6nF4r+kJrP6707zTpUWdwt6bnG6Ad8DEwwhjjrB6dLCIrPRWcqj5FRcVs2nSQSy5JsDsUt0VFhdO6dX1Wr860OxTljU7stRaqa3VbtTwuP7+Ir7/eyJtvrmThwl2IQP/+zfjPfy7n6qs7EB9fp1riEBGaNo3kqqs6cNVVHQCrbmjJkj3MmLGF6dO3MGPGFmAmSUlNGT++C+PGdaZxYz8aIg4KhqZXwp6pUJQPwb7Re608z92ennccm4eeJiLhxpg8Y0ySB+JS1Swt7SinThXSqVMju0OpkMTEGJYvT7c7DOWNMudaXz1cz7Nv3zFef3057767hgMHjtOiRV1eeOFSbrrpAmJiIj36bHdFRIRx2WWtuOyyVrzyylBSU7OYPn0LX36Zyp/+9AMPPfQjQ4a04sYbu3DVVR38YwgsbhTseN+q64rxjpouZT93k55/UGK3dGApkFi14Si7bNjgWzO3nBITmzBlygYOHz5ZqeEC5YcyfoQajaFuF480v2vXUV54YQnvvbeGwsJirryyLX/4QxKXXdaKoCDvnUUlInTq1IhOnRrxyCP9SU3N4tNP1/HppynceOM31Kv3Pbfe2o0770yiTRvv3o6mTE0uheCakD5Nkx51Wpnr9IhIExHpAdQUke4ikuh4XQzUqpYIVbVwztzq0ME3Zm45uRYzK3WaKbZ6eppcWuVbT2zffpjbb59G69b/5d13VzNhQle2bbuX774by9Chrb064SlNx44NeeaZwezYcR8LFtzCZZe14tVXl9O27WsMHfoJ06dvprjYB/e4C6kFMZfB3u90A1J1Wnk9PZcDE4A44GWX48eAv3ooJmWD1NQs4uKiqFOnegs+K8uZ9KxalcHgwS1tjkZ5jaPrIC/L2m+rimRlHedvf/uZ//1vJaGhwfzhD0k8/HA/4uL8oxg4KEi4+OIELr44gYyMY7z77mreemsVI0d+Qfv2Dfjzn/syfnwXwsO9e1++M8SOspKeI2uhfne7o1FeoMyeHmPMJGPMIGCCMWaQy2ukMebraopRVQNfm7nlFB1di4SEuqxYsc/uUJQ3yXBuPXFppZs6ebKA559fTKtWr/K//63k97/vwY4df+TVV4f5TcJTUkxMJI8/PpCdO+/js8+upkaNEG67bRotW77Kiy8uIScnz+4Q3RM7HBAr8VGK8oe3bnS8TRCRB0q+qiE+VQ2Kiw0bNx70iT23StOzZ1NWrNBiZuUi4weo0wlqNT3vJowxfPHFetq1e41HH53HoEEtWL/+Ll5/fbjXFCh7WmhoMOPGdWH16on88MONdOjQgIcfnktCwn945pmF3p/81GikG5CqM5S391aE42ttILKUV5lEZKiIbBaRbSLySCnnw0VksuN8sogkOI5Hi8gCEckVkddK3NNDRFIc97wqjnXXRaS+iMwRka2Or/XKi09Zdu06yokTBT7Z0wNW0rNrVzZZWcftDkV5g4JcyFoETYeddxPbtx9m6NBPGTfuKxo2jGDBglv47ruxPrNaeVUTES67rBVz597MihV30L9/M/7v/xbQosUrPPvsIo4d8+LkRzcgVS7KG956y/H1b6W9yrpXRIKB14FhQEdgnIh0LHHZbcARY0xr4N/AC47jp4DHgYdKafpN4A6gjeM11HH8EWCeMaYNMM/xvXKDs4jZ16arO/XsGQugQ1zKsn8BFOdDTMWTnvz8Ip57bhGdO7/J0qV7+O9/h7F8+e1cfHFC1cfpo5KSmjJt2jhWrLiDPn3ieOyx+SQkvMJzz3lp8hM7wvqaPsPeOJRXcGuXdRH5p4hEiUioiMwTkSyXoa9z6QVsM8bsMMbkA18Ao0pcMwqY5Hg/FRgsImKMOW6MWYyV/LjGEQNEGWOWGWMM8BEwupS2JrkcV+VwTlf3tZlbTj16xCCCDnEpS8b3EBJR4a0nlizZTWLiW/z1r/MZPrwNGzfezT339CI42K3/TQacpKSmzJhxA8nJt9O7dxx//et8WrR4hRdeWExubr7d4f0mqh3Ubg3p0+2ORHkBd/9rvswYkwNcCaQBrYE/l3NPLLDH5fu9jmOlXmOMKQSygbIWhoh1tFNam41dVorOBBqXE59ySE3NIiamNvXq+eY6N5GR4bRv34CVK3XaesAzBvZ9D40HQ3D5e1gBHDlykokTp9O//wccO5bPtGljmTr1Ov/doqGK9eoVy8yZN7Bs2W306hXLI4/Mo0WLV3jxxSWcOOEF2zKKWL09++dbQ58qoLmb9DjnKA4HvjTGZHsonirh6AUqdWEGEZkoIitFZGVWVlY1R+adfHXmlquePWNZsSIdo+txBLZjW+B4mlv1PMYYPvsshfbtX+f999fw0EN92LDhLkaMaOf5OP3QhRfGMWvWeJYuvY0ePWJ4+OG5tGz5Cv/9bzJ5eYX2Bhc3whryzJxjbxzKdu4mPTNEZBPQA5gnIg0pMfRUinQg3uX7OMexUq8RkRCgDnConDbjztHmfsfwl3MY7EBpDRhj3jbGJBljkho29O1f9FXBGENqahadOvn2z6Jnz6bs33+cvXtz7A5F2Wnf99bXmKFlXrZ9+2Euv/wTxo//moSEuqxcOZEXX7yM2rV1j6bK6t07jtmzb2TRoltp374Bf/zjbNq0+S/vvLOKgoIie4Jq2B9C6+gQl3Iv6THGPAL0BZKMMQXAcc6uzylpBdBGRFqISBgwFphW4pppWLu0A4wB5psy/lR3DF/liEhvx6ytmwHnXETXtm5xOa7KsHt3NseP++7MLaeePa2pyVrMHOD2fQ9R7aF2Qqmn8/OLePZZq1B52bK9vPbaMH755Xdn7VSuKq9//2YsWHALc+feRGxsFBMnzqB9+9f5+ONfKSoqrt5ggkKt3r99M63VulXAqkiFXnvgehG5GStBKXOpU0eNzj3AD8BGYIoxZoOIPC0iIx2XvQdEi8g24AFcZlyJSBrWKtATRGSvy8yvu4B3gW3AdsDxpx3PA0NEZCtwqeN7VQ7nzC1fT3q6dm1CSEiQFjMHssITjs0lS+/lWbx4N927v8Vjj83nyivbsmnTPdx9txYqe5KIMHhwS3755XfMmDGOqKhwbr75W7p0eZMvv9xQvdtbxI6AUwfg0PLqe6byOm6tJy4iHwOtgLWAs3/SOXvqnBw7s88qcewJl/engGvPcW/COY6vBDqXcvwQMLiseNTZ/CXpqVEjhAsuaMzy5drTE7D2/wTFeWfV8xw4cJxHHpnLBx+spXnzOsyYMY7hw9vaE2OAEhGGD2/LsGFt+OabjTzxxE9cd91UunVrwt//Pojhw9sgVbxH2lmaDgMJtoa4GvT27LOU13L3T5wkoJ8x5i5jzL2O1x89GZiqHhs2ZNG4cQTR0b6/f2zv3rEsX55e/V3nyjtkzLZ21W40AIDCwmJefTWZtm3/yyefrOPhh/uyYcNdmvDYKChIuOaajqxbdyeffHIVx47lMWLE5/Tp8x5z5+7w7ESEsHpWbY/W9QQ0d5Oe9YAOevshf5i55dS7dxy5ufmne69UgNn3PTQeBME1+OmnNLp3f4v77pvNhRfGkZLyB154YQgREVqo7A2Cg4MYP/4CNm68m3ffHUFGRi5DhnzMoEGTWLRol+ceHDsCjqbAcQ8+Q3k1d5OeBkCqiPwgItOcL08GpjzPOXPLX5KePn2syYJLl+4t50rld45tg9xt7Crow9ChnzBo0CSOHcvjm2+uZ/bs8bRr55sLb/q70NBgbrstkS1b7uG114axefMhBgz4kKFDP/FMfZ5zdea92tsTqNxNep7CWuH4WeAll5fyYenpxzh2LN/np6s7tWpVjwYNarFsmSY9gWbfyikAXHJjFitX7uPFF4eQmno3o0e393ytiKq08PAQ7r67F9u3/5F//WsIq1Zl0KvXu4we/QXr1u2vugdFtYXItjrEFcDcnbL+M9ZKzKGO9yuA1R6MS1WDDRuspYz8padHROjdO057egKEMYaFC3dx9dWTWfP9h2w/EM2Ee65m5877eOihvtSqFWp3iKqCatUK5cEH+7Jjxx/5xz8G8dNPaXTr9j8mTpzOwYMnquYhsSPgwE9QcKxq2lM+xd29t+7A2hvrLcehWOBbTwWlqoe/zNxy1bt3LJs2HeTIkZN2h6I8JCvrOK+8sozu3d9i4MAPWbZkG0Mu2E1s0nU8/vhAIiPd235Cea/IyHAee2wAO3fex5/+1JsPPlhL27b/5Y03VlR+okKsY3XmjB+rJljlU9wd3rob6AfkABhjtgK+uSW3Oi01NYsGDWrRsGGE3aFUGWddT3KyrtfjL5y1Z6+8soxLLplETMxL3H//D4SGBvPWW1eyY2kXwoLyqNFyZPmNKZ9Sr15NXnrpcn799U66d4/h7rtnkZT0DkuX7in/5nNp2M+ayaVDXAHJrXV6gDxjTL5zbNyxZYRucuTjUlMP+k09j1PPnk0JChKWLdvL0KGt7Q7H7xQXG/bsyWbXrmx2787myJGT5OTkkZ9fRGhoMCEhQadfERGhREaGU7t2GJGRYWe9dw4/5eUVcupUIXl5RRw7lkd6+jF2784mNTWL9esPkJycfnpoo2PHhjz8cD/Gj+9Cp06Ov7uS34KQSGvmlvJLHTs2ZO7cm5g6NZUHHviRfv3e509/6s3f/35JxYcxg0Kg6RXW6szFRRAU7JmglVdyN+n5WUT+CtQUkSFYqyJrmuzDjDFs2HCAG27oYncoVSoyMpzOnRtpXU8VKSwsZtmyvcyevY3Fi3ezZk0mOTl51fLs0NAg2rdvwPDhbRgwoDkDBzanVav6Z15kiiF9mrXwnJu7qivfJCJce20nhg5tzV/+MpeXX17GjBlb+eyzq+nRo2nFGosdAWmfwqFkaNjXMwErr+Ru0vMIcBuQAvwea5Xldz0VlPK8jIxcsrPz/Kqex6lPnzi++GI9xcWGoCCduXM+1qzJ4MMP1/LppykcOnSS4GChR4+mjB/fha5dG9OyZT3i4+vQoEEtoqLCCQ0NorjYUFhYTGFhMfn5RRw/XkBubj7HjuVx7NiZX63j+QQFCTVqhJx+1aoVSmxsJHFxUbRoUY+wsHL+Cj+YDKf2Q9zo6vnBKNtFRobzxhvDGTOmIxMmfEufPu/x7LODeeCBPu7/9x4zFCTEGuLSpCeguJX0GGOKReRb4FtjjK785gf8sYjZqV+/eN56axXr1x/gggsa2x2OzzDGMGfODp57bjE//ZRGWFgwo0e359prO3LppS2pW7dGmfcHBwvBwUGEh0NEhFWP4XF7v3VsJnmF55+lvMoll7Rg7do7ueOO6fz5z3NYtGg3H300mjp1yv73FICwOtbK3enTodtzng9WeY0yC5nF8pSIHAQ2A5tFJEtEnijrPuX9nNPV/a2mB2DAgOYALFyoq666a/XqDAYO/JDLL/+ELVsO8a9/DSEj40EmTx7DmDEdy014bGEM7P0GGl1s/RJTAad+/ZpMnXotr746lFmzttKz5zvur8geOwKyN0DuTs8GqbxKebO3/oQ1a6unMaa+MaY+cCHQT0T+5PHolMds2JBF/fo1adTIf2ZuOTVvXpf4+ChNetyQk5PHnXfOICnpbTZtOsibbw5nx44/8uCDfalfvxp6aiojZxMc26pDWwFORLj33guZP/9mcnLy6Nv3PebPdyORca7OrLO4Akp5Sc9NwDhjzOl/g4wxO4AbgZs9GZjyrJSUA3Tp0shvV6sdMKA5Cxfu8uwGhj5u/vyddOnyJu+8s5r77+/N1q33cuedSYSHu1vqZ7M9X1tf43SquoKLLmrO8uV3EBcXxeWXf8KkSWvLviGyFUR10KQnwJSX9IQaYw6WPOio69HlTn1UcbFh/Xor6fFXAwY0Z//+42zbdtjuULxOUVExTzyxgMGDP6JGjRCWLPkdL798uXu1EN5k92RrzZVacXZHorxEs2Z1WLLkdwwc2JwJE77jlVeWlX1D7Ag48DMU5FRPgMp25SU9+ed5TnmxtLSj5Obm06WL/xb5XnRRM0Dreko6fPgkV1zxGX//+0ImTOjGmjW/p3dvH0waslOt3bKbXW93JMrL1KlTg5kzb+Cqq9pz//0/8Oyzi859cewIKC6AjB+qL0Blq/L6sbuKSGkpsAA+9mehckpJsTbw8+eZTe3bN6BBg1osXLib225LtDuc6lV4wtp1/NhWoNhauC+iOTsONuaK4Z+xc+dR3n77Sm6/PdF3hzd3TQYEmo2xOxLlhcLDQ5gy5VpuvfU7HntsPkVFxTz++MCzL2zQB8KjrV3Xm11b/YGqaldm0mOM0aUq/VBKiv/O3HISkdN1PQGhKM+qcUn71Pqr1RSedUnksQieGd6atsMeocvgRPDVhMcYa2ir0UCoGWN3NMpLhYQEMWnSaIKChCee+On0ZqZnCAqGmCsgY5auzhwg3N17S/mRlJQDtGhR1+83ZhwwoBlpaUfZsyfb7lA8xxir12NGe/jlBji6DtrfD/0mw9DVcEUKaxpM5a5JV7N4e0dGX7iHLgfGwfddrb9ufdHRXyFnMzTXoS1VtqAg4b33RnLddZ146KE5vP32qrMvihsBeYfg4NLqD1BVO48mPSIyVEQ2i8g2EXmklPPhIjLZcT5ZRBJczj3qOL5ZRC53HGsnImtdXjkicr/j3FMiku5yTlcrO4eUlP1+Xc/j5Fyv56ef0uwNxFNOZsD8wbBkLIRGwcXfw6g06P4iNL8O6nfnx+U16Td6Ez/vHUzvP80j+Oq9cOH7Vh3DwpHw05WQm2b3J6mYXZNBgiH+GrsjUT4gJCSITz65iuHD2/CHP8xk2rTNZ14Qc7m1wKXO4goIHkt6RCQYeB0YBnQExolIxxKX3QYcMca0Bv4NvOC4tyMwFugEDAXeEJFgY8xmY0w3Y0w3oAdwAvjGpb1/O88bY2Z56rP5slOnCtmy5ZBfz9xy6tq1CdHRNZk71w8XH8ucB993s7Zh6PWW1avTdCjIb/9Jz527g5EjP6dt22h++ukWYmIiIaQmtLoVrlgH3f9lzVz5vhvs/srGD1MBxUWQ9gk0GQI1/Hd4VlWt0NBgJk8eQ48eMYwdO5Vly1z25guNsoZK06fZF6CqNp7s6ekFbDPG7DDG5ANfAKNKXDMKmOR4PxUYLFZl5SjgC2NMnmONoG2O9lwNBrYbYwKkaKNqbNyYRVGR8esiZqegIGHw4JbMmbPdv9br2fkxLLgcwhvA0BXQeuJZtQiLF+9m1KgvaNs2mnnzbqZhwxKLUAaFQocH4YoUiGoHi8fAyvug+OxaIK+SOQdO7IVWv7M7EuVjIiLCmDHjBmJiIhkx4nPS0o7+djJ2hGOxy232BaiqhSeTnlhgj8v3ex3HSr3GGFMIZAPRbt47Fvi8xLF7RGSdiLwvIvVKC0pEJorIShFZmZUVeNuIOYuYA6GnB2DIkJZkZOS6vzS9t9v8Giy92dp64bJlUKdk56m1WegVV3xKfHwUc+bcRHR0rXO3VzsBLl0E7e6HLa/CwtFQkOux8Cttx/vWbJtYXZBQVVyjRhHMmnUDBQVFjBr1BcePO1Ze0dWZA4ZPFjKLSBgwEvjS5fCbQCugG5ABvFTavcaYt40xScaYpIYNA697PCVlP+HhwbRpE213KNViyJCWgDXU4/O2vAGr7rW2Xbh4BoRGnnXJ7t3ZDB/+GXXr1mDu3Jtp3Lh2+e0Gh0GPf0PPNyHje5h3MZzywiTx1EFrg9GEGyHYv4vwlee0a9eAzz+/hvXrD3DLLd9avcC1W0CdTpr0BABPJj3pQLzL93GOY6VeIyIhQB3gkBv3DgNWG2P2Ow8YY/YbY4qMMcXAO5w9HKaweno6dGhISIhP5rsV1rx5XVq3rs+cOT6e9OyaAivvgbhR0P9LCD57mazs7FMMH/4Zx48XMGvWeOLioir2jDZ3woDvrE0Y5w2Ck5lVFHwV2fWZVYDdUoe2VOUMG9aGf/7zUr76aiP/+tcv1sHYEXBgEeQfLftm5dM8+ZtvBdBGRFo4embGAiUrxaYBtzjejwHmG6v4Yhow1jG7qwXQBljuct84SgxtiYjrgh1XAeur7JP4kXXr9gfM0JbTkCEt+emnNPLzi+wO5fzsXwBLb7S2XOj7OQSdvbxWfn4R11wzhU2bDvL119fRufN5/jOOvRIunmXtPD13IJzYV8ngq4gxsP09qJ8E9S6wOxrlBx54oA9jxnTk0UfnWet5xY6w1rfaN9vu0JQHeSzpcdTo3AP8AGwEphhjNojI0yLiHJB/D4gWkW3AA8Ajjns3AFOAVGA2cLcxpghARCKAIcDXJR75TxFJEZF1wCCsHeKVi/37c8nIyKV79yZ2h1KthgxpyfHjBSQn7y3/Ym+TuxMWjYHINjBwmjX7qgRjDBMnTmfevJ28++4IBg9uWblnNh4Eg36Ak/tg/qXWsJLdDvxsrUHUeqLdkSg/IWKt4dOqVX2uv34qmYWdrMkBOsTl1zw6xmGMmWWMaWuMaWWMecZx7AljzDTH+1PGmGuNMa2NMb0cO7g7733GcV87Y8z3LsePG2OijTHZJZ51kzGmizHmAmPMSGNMhic/my9as8YarujePbBWsR00qAVBQcKPP263O5SKKTxuFRabYmvYKazU2nyefvpnJk36laeeGsgtt3Srmmc36g8Dp8PxnfDTUPs3ZNz0svULKeFGe+NQfiUqKpypU68lO/sUt/5uBqbpcKuuzdtnMarzFhiFHQqAtWutpKdrV/+fru6qbt0a9O0bz8yZW+0OxX3GQPLtkL0e+n0Bka1Lvezbbzfx1FM/c/PNXXniiVL2FqqMxhdD/6lw5Ff4eQQUnqza9t2Vs8X667vNXaX2dClVGV26NObFF4cwe/Y2Zqe0h/wjkLXE7rCUh2jSE0DWrMkkIaEu9eoF3i+OkSPbsmZNpu9sSbH9Hdj1BVzwd2h6eamXbNyYxc03f0PPnk15660rPbN5aOxw6POxVeC5eAwU5Vf9M8qz+T8QFGYlPUp5wF139WTo0Nbc9PAJiqWGtY+d8kua9ASQtWszA66ex2nkyHYAZy9B742OpsCq+6DJZdDxrN1bAMjJyeOqqyZTs2YoX311HTVqlLl3cOUkjIVe/4N9s6w1goqrsSD81EHY8aE1rFUzsHooVfUREd5/fySE1Gbh1naYPV9bw8rK72jSEyByc/PZuvUQ3boFZtLTrl0D2raNZtq0LXaHUrbCk7D4egitC30+OmNbCafiYsPNN3/Dtm2HmTJlDPHxdTwfV+uJ1p5euyfDyrus4bfqkPocFOdBh4eq53kqYMXERPLOOyN498eWyMm9cGh5+Tcpn6NJT4BYt24/xhCwPT1gDXEtWLCTnJw8u0M5t18fg5yNVsJzjp6NZ59dxHffbeblly9n4MCE6outw0PQ6a+w7W349a+ef97xXbDlNWhxC9Tp4PnnqYB31VUdqNtxDPmFwexd+r7d4SgP0KQnQKxZY01mC7SZW65GjWpPQUExs2d76f46BxZa9Stt7oKYIaVeMmvWVp54YgE33ngB995rw/qbF/wD2vwBUp+H1H969lnrngQEuvzNs89RysVzL13Dku3tMLu+IveYF/+BpM6LJj0BYu3aTKKjaxIbe/bWBYGiT584oqNremddT0EuLJ1gLYff7YVSL9m27TA33PAV3bo18VzhcnlEIOk1aD4O1v4Ftr3jmeccTYGdH0G7eyEivvzrlaoikZHhxPa5lfj6h3nr+bftDkdVMU16AsSaNZl07x5jzy9KLxEcHMSVV7Zlxowt5OV52Tocax+G42nQ+0MIPXu/rNzcfEaP/oKQkCC+/vp6atUKrfYQT5Mg6DMJml4By38PaZ9VbfvFhZB8h7UuUcdHq7ZtpdzQdtCtFJkg8rdP8c1FTdU5adITAAoKili//gDduunsl7FjO5OdncesWV60Zk/mXNj6prXTeaOLzjptjOG226axceNBvvhiDAkJdW0IsoSgUGsPsEYDYelNsPPjqmt744twKBl6vgHh9auuXaXcFR6NaTiIsX03cccd03x3Cxt1Fk16AsDGjQfJyysK6Hoep0svbUmjRhF8+mmK3aFY8rNh2e8gqh10fabUS15+eSlTpmzguecGc+mlldxioiqF1IKLZ0KjQbD0FmtvrMo6sg5SnoRm10Hz6yvfnlLnKaTFtbRocBCOpvDii7pYob/QpCcArFhhbVDfs2dTmyOxX0hIENdf34kZM7aQnX3K7nBg9QNwMh16Typ1teEFC3by8MNzueaaDvz5z31tCLAcIbWs7SpiLrNWkF735PlPZz91wFoAMaw+JL1etXEqVVFxo0GCeGLCQZ5+eiGbNnnBHnSq0jTpCQDLl6dTt24NWrfWoQKA8eO7kJdXxFdfbbQ3kPSZsON96PAXaHDhWaf37Mnm+uun0q5dNB98MMp767FCalqJT8tbYf3T1nBXQW7F2sjPemjvgQAAIABJREFUhgVD4cReuOgrqNHAM7Eq5a6ajaHhRYzusZ6IiFAmTpxOcXE1rU+lPEaTngCwYsU+kpKaeu8vzWrWq1csrVrV47PPbBziyjts9YzU7QJdnjz7dF4hY8Z8yalThXz99fVERobbEGQFBIXChe9ZU9rTPoPZiXBohXv3nsyAn4dbM7Yu+goa9vNsrEq5K34MIcc38d7LbVi0aDfvvLPK7ohUJWnS4+dOniwgJeUAvXrZNLSVdwh2TIJF18LMTvBdC+v18yhI+bu1mWQ1ExFuuKEL8+fvZO9em3YPX3kv5B20hrWCz05o7rtvNsuXp/Phh6Np395Hej1EoPNjMHgBFJ2CH/tYiV1uWunXGwN7voFZXeDwauj7KTQdVq0hK1Wm+KsAGN1jA5dc0oJHH51HVtZxm4NSlaFJj59buzaTwsJievaMrd4HF56AlL/Bt81g2QQ4uAQi2/H/7d13eFRV+sDx75sEkhBKKKFFmoBAQGpAFBARpIiKurrSFBF111VXYS2w4lp+FlxRXNZ1UZFqQUUEVhQEsYWe0KQFQ5EEIfTek/f3x7loAgkQMpM27+d55snMveeee+bmZvLOveecl4pXQ4XWcHC967D6RT2Y2xlSv83T5vXr1wQgf765JU+BXz6ERk9DuWZnrR4zZhlvv53A4MFtuPXWQjgTcaX2cP0KN8niponwxWXu1tXa4S7I2TzJnRszGsCPt0JETei6FGr8Mb9bbkxmJaKhwlVI8mTefLMbBw+eYMiQb/K7VSYXRPMqh04BFBsbq/Hx8fndDL8aOXIRjzwyk5SUgURHl86bne5cAPPugCPJUP12aPAElGvhrgRkdHQ7bBjthmsf/RVq9IRmw90HTR7o3v1DEhJ+ZcuWgRQvHpwn++TYTnfFK6I6dF7gbgtlEB//K23bjqFduxrMnNmH4OBC/r3kSAqsGwG/fuXSa/xG3HD3mr1dmong4vnWRGPOKXGkSwDcfTVPvLiVV1+dz4IFA2jd+pL8bpnJgogkqGpstust6CnaQc+dd37O3Lmb2Lp1UN7scNP7sGgAlLjETbSXxbwzZzl1FNa84lIbBIdD67FQ7Wa/N3XmzCS6dfuADz+8lV69Lvf7/lCFuNth6//clY3IhplW79p1hBYt3AywCQn3U6FCCf+3KS8d3QbHUiEoFEIrQFhUfrfImPM7uh2mRkPM3zl46VAaNPgPFStGsGTJfYX/S0kRdL6gx35jRdzixVvzbqj62tfcyJ0KV0GXxRcW8IAb/dP4Wei+CkrVgR9vgYSBkH7Sr83t3Lk2deqU4803L7DDbW5tmgDJn0Hj588KeE6cSOP22z8lNfUQn332x6IX8ACEV4GyTV3yUAt4TGERXhkqXQu/fEipksV5/fUuLFu2nbfftk7NhZEFPUXYvn3HWL9+d94EPRvGwLLH3O2sDrMgtHzO6yhVB66Lg8sedok3v+vuhjL7SVCQ8OCDLZk/P5mlS7f5bT8AHEyC+IfcLZ36j2Vapar86U9f8N13mxk9+iZiY20+JWMKlBq94dBG2L2E22+PoWPHWjz11Fx27LBOzYWNX4MeEekqIokikiQig7NYHyoiH3vrF4lIzQzrhnjLE0WkS4blm0XkJxFZLiLxGZaXE5HZIvKz97OsP99bYZCQ8CuA/zsxJ0+FxfdB5c5w5fu5658RHAqxI93w59RvYXYbOJzsu7aeoX//ppQsWZxhw+L8tg/ST8L8PiAhcOVECMrcf2jYsDjGjVvOM8+0p2/fxv5rhzHm4lS71d2W/eVDRIR//7sbhw6dYPDgOfndMpNDfgt6RCQY+A/QDYgBeolIzBnFBgB7VbUOMAJ4xds2BugJNAS6Am959Z3WQVWbnnHfbjDwjarWBb7xXge0hQtdojy/XjnYvw4W9IWyLdwcK77qkFr7Hugw03WGnt0GDvgnM3qZMmEMHNiaTz9dw7Jlfrra89NzsHsxXPHOWRnDP/lkNX//+1x6976cZ55p75/9G2Nyp3gZiO4Ov0yC9FM0aBDFoEGtGTt2OQsW+O9LmfE9f17paQUkqepGVT0BTAJ6nFGmBzDeez4Z6ChuBr0ewCRVPa6qm4Akr75zyVjXeMD/PWELuHnzkomJiaJcubPTG/jEqcMubUBwOFw9Jcvs4LlSuSN0/M7N+TK7rZvLxQ8GDbqSyMgwnn7aD8Pmd/wAq19ysxVXvz3TqoULU7jrrs9p06Ya7713k00eaUxBVrOP64i/3Q1Zf/rp9kRHl+Ivf/mStLT0fG6cuVD+DHqigYwhcIq3LMsyqnoK2A+UP8+2CnwtIgkicn+GMpVU9fRX9e1AlinFReR+EYkXkfidO3fm/F0VEunpyvz5ybRpU+38hS/Wkgdh/xq46kM3WssfyjVz/XyCS8Cca1wQ4WORkWE88cRVzJjxs2+/tZ3YB/PvhJK1ocXITKuSkvZw000fER1dms8/v4OwsBDf7dcY43tVu0PxsrBpHAAlSxZnxIguLF++nVGjivYo4KKkMHZkbquqzXG3zR4UkavPLKBuHH6WY/FV9R1VjVXV2KioojuCZPXqHezff5y2bav7ZwfJU2DTeGg0FKpc5599nFb6Mhf4lIiGb7u4nFU+9te/XkHFihE88cQc3+TXUXVD94/+6mYaznAVLDl5Px07TiA9XZkxozdRURG5358xxr+CQ12H5pSp7gsNcNttMXTqdKl1ai5E/Bn0bAUyXma4xFuWZRkRCQHKALvPta2qnv65A/ic3297pYpIFa+uKsAOH76XQmfePHfFwi9Bz7FdsOQBKNvMzSqcFyKqQacfoExD+OFml9/Jl9VHFOfllzsSF7fFN9/a1rziAsOmw6DC73dmU1MP0anTRPbtO8bXX99ZeFJMGGPg0n7udvuWTwB+69R85MhJnnzSOjUXBv4MepYAdUWklogUx3VMnn5GmelAP+/5bcBc7yrNdKCnN7qrFlAXWCwiESJSCkBEIoDOwKos6uoHTPPT+yoU4uK2ULlySWrVivR95QkPw4m9bvLBM2YU9quwKOg41yWknN8X1r/l0+r7929K5861eeKJ2WzevO/iK9r2Nax8ys0wXf/3SSFTUg7Qvv04UlIO8OWXvWnevIoPWm2MyTPlYqFMjMsn6KlfvwIDB7Zm3Ljlvw0eMQWX34Ier4/OQ8AsYC3wiaquFpHnReQmr9h7QHkRSQIG4Y24UtXVwCfAGmAm8KCqpuH66cSJyApgMTBDVWd6dQ0DrhORn4FO3uuAFRe3hbZtq/u+c+zWL9wIhoZDoWw+DK8uVhqu+Qqib4D4B2HVC+5Wkg+ICO++eyMiwr33Tr+421z710LcHe6K1BWjf0u9sWHDHtq1G8u2bYeYNasvbdr46bajMcZ/RFzalF3zMyVLHjr0aqpWLcVDD1mn5oLOr316VPVLVb1MVWur6ovesn+o6nTv+TFVvV1V66hqK1XdmGHbF73t6qnqV96yjaraxHs0PF2nt263qnZU1bqq2klV9/jzvRVkKSkH+OWX/b7vxHzqKMT/FUo3gJh8nBEgJNwNj6/ZF1Y+7SZF9FHgU716GV5/vTPffLOJxx77OmcbH90O33Vz9/6vng4hrq/O/PnJXHXVGA4cOM7cuXf5r5+VMcb/avYFCYaNY35bVKpUKMOHX0dCwjbee29ZPjbOnE9h7MhszmPevC2AH/rzrHkFDm+Clv/J/wSRQcXgyvFu9uZ1r7tOw+mnfFL1vfc255FHrmDEiIWMGLHgwjY6eQC+v8ElFG3/BZSsCcC4ccvp0GE8pUuHEhfXnxYtbLZlYwq1ElXdleaNYyHtxG+Le/ZsxNVX12DIkG/YvftIPjbQnIsFPUXQvHnJlChRjCZNshy1f3EObnAJQWv0gkodfFdvbkgQtPgXNHrGfQDF/dF1MsxttSK89lpn/vCHBgwa9DVvvLGQcybmPXkQvu0Ge1dA24+hfCy7dh2hZ8/J9O8/jXbtqrNo0b00aFB0RwsaE1Bq3w/HdsDW37uOnu7UvH//Mf/M+WV8woKeIuj773+hdetLKFYs+PyFL9Syx93VlWbDfVenL4i4ZKXN34CUz92Q9qOpua42ODiIiRNvoUePegwcOItevT7j0KETZxc8edDd0tq9CNp+zMmK3Rg9eimNGr3FlClreeGFDsyc2dd/E0QaY/JelS5QojokvZNpcePGlXjwwZaMGhXv/3x+5qJY0FPEpKYeYuXKVDp1quW7SnfEuYAi5kl3abcgqv+Imw9n92KY2QJ2Lc51leHhxZgy5Q5eeulaPv10DbVrj+TFF3/4/dL1ka0w52rYtZDUuqMZMbkK9eq9yX33/Y8aNSJZsuQ+nnrqakJC7M/MmCIlKBhq3wvb57ir4Bk891wHKlQowUMPfembOb+MT9mncREzd+4mADp1utQ3Faq6qzzhVTMNvy6QavaG6+a7K1Jz2sLqYZCelqsqg4KEIUPa8eOP/WnRogpDh35LhQqvckObx9n5weUcTl3H3ePupXLLXxg06GsqVCjB//7Xi4ULB9CkSWUfvTFjTIFT+x7XofmMqz2RkWG88konFixI4f33V+ZT40x25Jx9FYq42NhYjY8vWtOHDxgwjSlT1rFr1+MEB/sgpt3yqesrc8VoqD0g9/XlheN7YPGfIHmym9On1Ttubg0fWLliG6k/vMA1ke+w50gphs4exMGQ+rRqFU2PHvWoXbucT/ZjjCkEfrgVdnwPN6e4UaWe9HTlqqveY/PmfSQmPkSZMmH52MjAIiIJZyQjz7zegp6iE/SoKjVqvEHLltF89tkfc19h2gmYEeMSinZb7i7pFhaqsPkDiH8YTh10o7waPQ2huQhK9q6AhEdhx3dwSQ9oNRrCbEZlYwJW6vfwzTXQ6l2oc2+mVfHxv9Kq1bs8+mhrXn+9S/60LwCdL+ix21tFSFLSHpKTD/iuP8/P/4VDG6DZPwtXwAPeJGJ94cb17t574r9gWnVIGASHNuesrr3LYcHd8FUz2LfSfcC1+9wCHmMCXcWrIbIxrB951lxhsbFVue++5owcuYhVqwI6K1KBYkFPETJnjpvb0Sf9eU7sg9X/B5U6QpWuua8vv4RFQatRcP1KuORW9+E0vRbMau36/KR+61JqnP7AUnWvd/zgZnue2coFO1s+hgZ/g5uS3Dc6X890bYwpfESg3iOw7yd3BfgML77YkTJlwnjggRnWqbmACMnvBhjfmTNnE9WqlaZOHR/0K1kzzPWNafZq0fgHH9kIrpoATV6AXz5yfZVWDPl9vYRAsVJw6jCkZxiaXraZmwuoZt/c3RozxhRNNXrB8ifc1eQz5jCrUKEEr756HQMGTGfs2GUMGNA8nxppTrOgp4hIS0vn2283cfPN9XOfb+vwFlj3hvtHX66ZbxpYUERUd0PvY5502eL3JLhbVif2ulmVQyIgrBKUqgNRbS3QMcacW0g41PkzrH4JDiRC6XqZVvfv35Tx41fw+OOzufHGelSsGJFPDTVgQU+RMX9+Mnv3HqNLl9q5r2zFUPezyQu5r6sgC6sAVbu4hzHGXKx6f4V1r8Gaf0Lr9zKtEhFGjepOkyajeOyxr5kw4ZZ8aqQB69NTZEyfnkixYkF061Y3dxXtWQab33f3qSMsMaYxxpxXWEW4dABsnghHUs5a3aBBFE880YaJE1f+NpeayR8W9BQBqsq0aYl06FCL0qVDc1ORm4gwtBw0HHL+8sYYY5wGj4Gmw9rXs1z91FPtqF27LA88MIPjx32THNnknAU9RUBi4m5+/nkPPXrUO3/hc9k2E1K/gYZPQ/FI3zTOGGMCQcmaUKM3bHjH9Rc8Q3h4Md56qzvr1+9m2LC4vG+fASzoKRKmTVsHwI03XnbxlaSfgqV/g5J1oO4DPmqZMcYEkIaD4dQRWPtqlqs7d65Nr16NeOmlOJu7J59Y0FMETJ++nubNq1CtWpmLr2TDaDiwFpq9AsHFfdc4Y4wJFGVioGYfWP9vOJp1lvV//asrZcqEcvfdUzl5Mne5AU3OWdBTyKWmHmLBguTc3do6eQBW/gOi2sElNrLAGGMu2uXPuLm+Vr+U5eqoqAj++9/uJCRs45VX5uVx44xfgx4R6SoiiSKSJCKDs1gfKiIfe+sXiUjNDOuGeMsTRaSLt6yaiHwrImtEZLWIPJKh/LMislVElnuP6/353gqKqVPXoUrugp7Vw+D4Tmj+etGYiNAYY/JLqTpw6T2Q9DYc/iXLIn/4Qww9ezbi+ee/Z+XK1DxuYGDzW9AjIsHAf4BuQAzQS0TOTHU9ANirqnWAEcAr3rYxQE+gIdAVeMur7xTwN1WNAVoDD55R5whVbeo9vvTXeytIJk5cSUxMFI0bV7q4Cg5vgcQR7pJs+WxztBljjLlQjYYCQb/PeZaFN9/sRrly4fTrZ7e58pI/r/S0ApJUdaOqngAmAT3OKNMDGO89nwx0FDedcA9gkqoeV9VNQBLQSlW3qepSAFU9CKwFov34Hgq0jRv3Mm9eMnfe2fjiZ2Fe8Xf3s0nWl2KNMcbkUER1qD/IzXm2a1GWRcqXL8GoUTewfPl2XnrpxzxuYODyZ9ATDSRneJ3C2QHKb2VU9RSwHyh/Idt6t8KaARnPqIdEZKWIjBGRslk1SkTuF5F4EYnfuXNnTt9TgfL++ysRgT59Lr+4CnYths0fQL2BNhGhMcb4UsMhEFYZEh49KwP7aTffXJ8+fS7nhRd+ZNmyrDs+G98qlB2ZRaQk8BnwqKoe8Bb/F6gNNAW2Aa9lta2qvqOqsaoaGxUVlSft9QdVZeLElVxzTc2LG7WlCssGuZlEG57V3coYY0xuFCvlrqDvXgibP8y22MiR3YiKKkHv3lM4fPhEtuWMb/gz6NkKVMvw+hJvWZZlRCQEKAPsPte2IlIMF/B8oKpTThdQ1VRVTVPVdOBd3O21ImvRoq0kJe2hb9/GF1fBpomwc577oyxW2reNM8YYA5f2g3KxsOwxl9Q4C+XKhTNx4i0kJu7i0Udn5nEDA48/g54lQF0RqSUixXEdk6efUWY60M97fhswV1XVW97TG91VC6gLLPb6+7wHrFXVTHN9i0iVDC9vAVb5/B0VIOPHLycsLITbbjuzb/gFOLEPlj8O5VvDpf193zhjjDEgQdDqbTc6dtmT2Rbr2PFSBg9uy+jRy/jkk9V52MDA47egx+uj8xAwC9fh+BNVXS0iz4vITV6x94DyIpIEDAIGe9uuBj4B1gAzgQdVNQ1oA9wJXJvF0PR/ishPIrIS6AAM9Nd7y2979x5lwoSV9OzZ6OJyba18Go7vgpZvuT9KY4wx/lGuuevUvOFdSP0+22LPPXcNV1wRzX33/Y+ff96dhw0MLKLZdLAKBLGxsRofH5/fzcix4cPn8/jjs1m+/E80aVI5ZxvvWgyzr4Q6D0DLN/3TQGOMMb87dQS+vBwIguuXQ0hElsW2bNlP8+ZvU6VKKRYuHEBEhM2On1MikqCq2c6/Yl/zC5lTp9L5978X0759jZwHPGnHYdE9EF4VmrzonwYaY4zJLKQEXDEGDm1wo7myUb16GT766A+sXr2D++//gkC+KOEvFvQUMtOmrWPLlv08+mjrnG+8+kXYvxpavg3Fc5GnyxhjTM5Uag8xT7o8h1s+y7bYddfV5oUXruXDD39i+PD5edjAwGBBTyGiqowYsZBatSJznlF973JY/TLU7AvRAZGhwxhjCpbGz0O5lrD4vmxTVAAMGdKWO+5oyJNPzmHq1HV52MCiz4KeQmT27I3Mm5fMoEFXEhycg1/dqSMwrxeERUGLN/zXQGOMMdkLKgZtPgRNhx9udp/NWRARxo7tQcuW0fTpM4WlS23iQl+xoKeQSE9Xhgz5hpo1I7nvvuY523jpQDiQCFdOhNDy/mmgMcaY8ytVB676EPaugIX3ZDtbc3h4MaZOvYMKFUrQrdsHNqLLRyzoKSQmT17D0qXbeP75awgNDbnwDbd8BknvQIPHoXJHv7XPGGPMBYq+Hpq+DFs+hp+ey7ZYlSqlmDWrL2lp6XTu/D6//nowDxtZNFnQUwicPJnG0KFzadSoIr175yDP1r7VsPBuKN8KGv+f39pnjDEmhxo8AZfeDaueg/X/ybZY/foV+OqrPuzceZhOnSawffuhvGtjEWRBTyEwfPh8fv55Dy+/3PHC+/Kc2OvuGYdEQLvPINjmezDGmAJDBFq9C9E3QfzD58zP1bJlNDNm9OaXX/bTocN4tm2zKz4Xy4KeAm7t2p08++z33HZbDDfccIEjttJOQNwdcOQXaDcFSlzi30YaY4zJuaAQaDMJKraHBXfChrHZFm3fviYzZ/YhOXk/7duPY/PmfXnY0KLDgp4CLC0tnXvumU6pUsV5881uF7ZRehosuAu2z4ZW70DUVf5tpDHGmIsXEg7XfAGVOrnJYxNHZlu0XbsazJrVl507j3Dlle+xbJmN6sopC3oKsGHD4li4MIWRI7tRqVLJ82+gCvEPuc5xzV5194uNMcYUbCER0H46XHILJDzibneln8yyaJs21Zk37x6KFQvi6qvHMX16Yh43tnCzoKeAmjp1HUOHfkvv3pfTq1ej82+QfspNeJU0ys362eAx/zfSGGOMbwSHQttPof7fYP2b8G1XOLYjy6IxMVEsXHgv9eqVp0ePSQwdOpe0tPQ8bnDhZEFPAbRixXb69p1Cy5ZVGT36RkTk3BukHYN5d8CG96DR09Dk5bxpqDHGGN8JCobmw6H1ONg5zyUp/fWrLItWrVqKuLh7GDCgGS+++COdOk1ky5b9edveQsiCngJm1aoddO36AZGRYUyb1pPw8GLn3uDwFpjdDpKnQPMRbprz8wVJxhhjCq5L+0HXJRBaEb67HhbdC8fPnpwwLCyE0aNvYsyYm4iP/5XLL/8vY8cus0Sl52BBTwGyePFW2rcfR1CQ8PXXd1KlSqlzb/DrVzCzhZttud0UqJ999l5jjDGFSOTlLvBp8DhsHAdf1IOfR2XZ16d//2asXPlnmjSpxD33TKddu7GsWLE979tcCFjQUwCoKhMmrODaa8cTGRlGXFx/YmKist/g+B5Y0M99AwirBF3jodoteddgY4wx/hccBs3+Cd2WQekYWPIAfNEANk44K/ipVass3313N6NH30hi4m6aN3+Hu+76nKSkPfnU+ILJgp58tnv3Efr0mUK/flOJja1KXFx/atUqm3XhU0dh7WvwxWVuIquGT7mAp3QOM64bY4wpPCIvh07fQ/svoFgpWNgPptWE1S/B0d+HrQcFCQMGNCcx8SEGDmzN5MlrqF//Tfr0mcLixVvzr/0FiATyvb/Y2FiNj4/Pl30fOXKSkSMX8fLLcRw+fILnnruGwYPbZj3j8rFdsOFdN1X50a1QubOL/ss2yfuGG2OMyT+aDttmwboRbj42CYLKXaBGT4junimp9LZtBxk+fD6jRy/jwIHjtGhRhTvvbEzPno0ubBqUQkhEElQ1Ntv1FvTkXdCjqqxZs5PRo5cyfvwK9u49xo03XsbLL3ekYcOKmQufPOD67Gz5BLbOgPTjUKkjNBoKla7JszYbY4wpoA4kwqYJ7nEkBSQYyl8BFdtBVFuocBWEluPgweOMH7+CMWOWsWzZdkTgyiur0b17Xdq3r0Hz5lXOP2imkMjXoEdEugL/AoKB0ao67Iz1ocAEoAWwG7hDVTd764YAA4A04K+qOutcdYpILWASUB5IAO5U1RPnap+/gx5VJTn5AIsWpRAXt4UZM35mw4a9FCsWxC23NODhh1vRtm11N8fOkS2wZynsiYfU72HPEtA0CKsM1W+Hun+GMjF+a6sxxphCStNhTwKkTIPUue7/yOk+P2ViILIxlK4PpRuQtDOKj2cc4/P/JZOQ4G6NhYQE0bRpZVq3jiY2tip16pSjdu1yVKoUcf4pUwqYfAt6RCQYWA9cB6QAS4BeqromQ5m/AI1V9c8i0hO4RVXvEJEY4COgFVAVmAOc7riSZZ0i8gkwRVUnicgoYIWq/vdcbfRX0PPggzOIj99GYuIu0o4foHH1VCqXPcFVsSW5olk4TRsUp2TwLji0yT2OJIOe8g5KCJSLhcrXQpUuUKGNm7vBGGOMuRCnjsDuJbAzDnbNh/1r4fBmIMP/+2JlOFW8MnuPlSNldwSbtkLS5lPs3F+MA0dD2X8klBMaQalyFYgsV4ZSZUpSOrIkpcuWJrJsKULDwwmLKEFYeAnCI8IpUSKUEiXDCA8vTnBwMCHFggkOFkJCgggODvJ+CkFB4tdA6nxBT4jf9uwCliRV3eg1ZBLQA1iToUwP4Fnv+WTgTXFHowcwSVWPA5tEJMmrj6zqFJG1wLVAb6/MeK/ecwY9/pKScpDSpUPp27cx116+lVtLnTFZ4GaBsIoQUQsqtIaSvdzzsk1ch7XgsPxotjHGmKIgpARUau8ep506CgfXw4F1cGgjHN1GyNFfiTr6K1FhSTSL2oc2OoBoDmZ2Puw9dmW9Oi1dSE8XFEhPF7q82pfv1tQiOFhYtOheWrSomos3eXH8GfREA8kZXqcAV2RXRlVPich+3O2paGDhGdtGe8+zqrM8sE/19OWSTOUzEZH7gfu9l4dExBeJSyqQxa99zhz4T5bFFUj1HguzLFHIZXk8Apgdj8zseGRmxyMzOx6ZFeLjoWS6usR4ANLSIDb2mYut9HzHo8a5NvZn0FMgqeo7wDu+rFNE4s91OS3Q2PHIzI5HZnY8MrPjkZkdj8zseGSW2+Phz3l6tgLVMry+xFuWZRkRCQHK4Do0Z7dtdst3A5FeHdntyxhjjDEBzJ9BzxKgrojUEpHiQE9g+hllpgP9vOe3AXPV9ayeDvQUkVBvVFZdYHF2dXrbfOvVgVfnND++N2OMMcYUMn67veX10XkImIUbXj5GVVeLyPNAvKpOB94DJnodlffgghi8cp/gOj2fAh5U1TSArOr0dvkkMElEXgCWeXXnFZ/eLisC7HhkZscjMzsemdnxyMyOR2b8Bc6NAAAIQUlEQVR2PDLL1fEI6MkJjTHGGBM4LPeWMcYYYwKCBT3GGGOMCQgW9OSSiHQVkUQRSRKRwfndnrwmItVE5FsRWSMiq0XkEW95ORGZLSI/ez+zSR1f9IhIsIgsE5EvvNe1RGSRd4587HXCDxgiEikik0VknYisFZErA/z8GOj9rawSkY9EJCyQzhERGSMiO0RkVYZlWZ4P4oz0jstKEWmefy33j2yOx6ve38tKEflcRCIzrBviHY9EEemSP632n6yOR4Z1fxMRFZEK3uscnx8W9OSCl2rjP0A3IAbo5aXQCCSngL+pagzQGnjQOwaDgW9UtS7wjfc6UDwCrM3w+hVghKrWAfbicsoFkn8BM1W1PtAEd2wC8vwQkWjgr0CsqjbCDcjoSWCdI+OArmcsy+586IYbvVsXN6lsvsyy72fjOPt4zAYaqWpjXOqlIQDeZ2tPoKG3zVve/6GiZBxnHw9EpBrQGdiSYXGOzw8LenLnt1QbXnLT06k2AoaqblPVpd7zg7h/aNG44zDeKzYeuDl/Wpi3ROQSoDsw2nstuBQpk70iAXMsAESkDHA13mhKVT2hqvsI0PPDEwKEe/OKlQC2EUDniKr+gButm1F250MPYII6C3HzsVXJm5bmjayOh6p+nSHDwELc3HOQIUWTqm4CMqZoKhKyOT8ARgBPkHmK5xyfHxb05E5WqTayTH8RCESkJtAMWARUUtVt3qrtQKV8alZeewP3h3k6gc0Fp0gpomoBO4Gx3i2/0SISQYCeH6q6FRiO+7a6DdgPJBDY5whkfz7YZyzcA3zlPQ/I4yEiPYCtqrrijFU5Ph4W9BifEJGSwGfAo6p6IOM6b/LIIj83gojcAOxQ1YT8bksBEgI0B/6rqs1w6Qkz3coKlPMDwOur0gMXDFYFIsjiUn4gC6Tz4XxE5ClcF4IP8rst+UVESgB/B/7hi/os6MmdC0m1UeSJSDFcwPOBqk7xFqeevszo/dyRX+3LQ22Am0RkM+5W57W4/iyBnCIlBUhR1UXe68m4ICgQzw+ATsAmVd2pqieBKbjzJpDPEcj+fAjYz1gRuRu4Aeijv0+oF4jHozbuS8IK77P1EmCpiFTmIo6HBT25cyGpNoo0r8/Ke8BaVX09w6qMKUYCIi2Iqg5R1UtUtSbuXJirqn0I4BQpqrodSBaRet6ijriZ1gPu/PBsAVqLSAnvb+f08QjYc8ST3fkwHbjLG6XTGtif4TZYkSUiXXG3yW9S1SMZVmWXoqnIUtWfVLWiqtb0PltTgObeZ0vOzw9VtUcuHsD1uN71G4Cn8rs9+fD+2+IuRa8ElnuP63F9Wb4BfgbmAOXyu615fFyuAb7wnl+K+2BKAj4FQvO7fXl8LJoC8d45MhUoG8jnB/AcsA5YBUwEQgPpHAE+wvVnOun9AxuQ3fkACG6E7AbgJ9yot3x/D3lwPJJwfVVOf6aOylD+Ke94JALd8rv9eXE8zli/GahwseeHpaEwxhhjTECw21vGGGOMCQgW9BhjjDEmIFjQY4wxxpiAYEGPMcYYYwKCBT3GGGOMCQgW9BhjfMLLfvx+htchIrJTvGzzF1FfpIj8JcPra7KrS0S+E5HY89RXWUQmicgGEUkQkS9F5LJz1WuMKVos6DHG+MphoJGIhHuvryN3s8VGAn85b6kL4E0E+DnwnarWVtUWuMzVuc75lWEmZWNMAWdBjzHGl77EZZkH6IWbaAwAESknIlNFZKWILBSRxt7yZ0VkjHe1ZqOI/NXbZBhQW0SWi8ir3rKSIjJZRNaJyAdeMEOGfdwjIm9keH2fiIwAOgAnVXXU6XWqukJVfzxXvSLyDxFZIiKrROSdDMu/E5E3RCQeeEREWnrva7mIvCoiq7xywd7rJd76P3nLq4jID175VSLSLveH3hhzPhb0GGN8aRJumvwwoDGwKMO654BlqtoYl0BwQoZ19YEuQCvgGS+f22Bgg6o2VdXHvXLNgEeBGNwsxm3O2P8nwI3e9gD9gTFAI1w28+xkV++bqtpSVRsB4bhcSKcVV9VYVX0NGAv8SVWbAmkZygzATY3fEmgJ3OelD+gNzPLKN8HNumuM8TMLeowxPqOqK4GauKs8X56xui0u7QKqOhcoLyKlvXUzVPW4qu7CJZvM7rbTYlVNUdV0XKBQ84z9HwLmAjeISH2gmKr+dAFNz67eDiKySER+wiWQbZhhm4/B9T0CSqnqAm/5hxnKdMblBlqOCwDL4/IlLQH6i8izwOWqevAC2miMySW7F22M8bXpwHBc/rHyF7jN8QzP08j+s+lCyo3GXUlah7sCA7Ca3xN6XlC93tWqt3D5fJK9ACUsQ7nD56jvNAEeVtVZZ60QuRp3K3CciLyuqhPO2toY41N2pccY42tjgOeyuMLyI9AH3EgsYJeqHjhHPQeBUjnduaouAqrhbiGd7lM0FwgVkftPlxORxufpS3M6wNklIiXJJmhS1X3AQRG5wlvUM8PqWcADp2+3eaPFIkSkBpCqqu/igrTmOXqTxpiLYld6jDE+paopwMgsVj0LjBGRlcARoN956tktIvO8TsFfATNy0IxPgKaquterS0XkFuANEXkSOIbL1vwoEJ3N/veJyLu4bOjbcbeksjMAeFdE0oHvgf3e8tG4W2VLvU7QO4GbcVfBHheRk8Ah4K4cvDdjzEWyLOvGmCLHm3dnhKp+k0f7K+n1J0JEBgNVVPWRvNi3MebC2e0tY0yR4U1ouB44mlcBj6f76eHnQDvghTzctzHmAtmVHmOMMcYEBLvSY4wxxpiAYEGPMcYYYwKCBT3GGGOMCQgW9BhjjDEmIFjQY4wxxpiA8P+s7ZtG1A+GWgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 648x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RcaI9ovxZbCJ"
      },
      "source": [
        "From the plots above we can conclude that:\n",
        "\n",
        "Recent clients are more likely to churn.\n",
        "\n",
        "Clients with higher MonthlyCharges are also more likely to churn.\n",
        "\n",
        "Tenure and MonthlyCharges are probably important features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_gd9wufZbCJ"
      },
      "source": [
        "##### Categorical feautures\n",
        "This dataset has 16 categorical features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LBiGl29ZZbCK"
      },
      "source": [
        "###### Gender and Age(SeniorCitizen) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXlIs06FZbCK",
        "outputId": "e4652935-a2b7-4898-d6f7-67d1b6f72b8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "def barplot_percentages(feature, orient='v', axis_name=\"percentage of customers\"):\n",
        "    ratios = pd.DataFrame()\n",
        "    g = df.groupby(feature)[\"Churn\"].value_counts().to_frame()\n",
        "    g = g.rename({\"Churn\": axis_name}, axis=1).reset_index()\n",
        "    g[axis_name] = g[axis_name]/len(df)\n",
        "    if orient == 'v':\n",
        "        ax = sns.barplot(x=feature, y= axis_name, hue='Churn', data=g, orient=orient)\n",
        "        ax.set_yticklabels(['{:,.0%}'.format(y) for y in ax.get_yticks()])\n",
        "    else:\n",
        "        ax = sns.barplot(x= axis_name, y=feature, hue='Churn', data=g, orient=orient)\n",
        "        ax.set_xticklabels(['{:,.0%}'.format(x) for x in ax.get_xticks()])\n",
        "    ax.plot()\n",
        "barplot_percentages(\"SeniorCitizen\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzhoZ3qzZbCN",
        "outputId": "6155aa17-8e60-443a-af5c-24a53b1ce7ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "df['churn_rate'] = df['Churn'].replace(\"No\", 0).replace(\"Yes\", 1)\n",
        "g = sns.FacetGrid(df, col=\"SeniorCitizen\", height=4, aspect=.9)\n",
        "ax = g.map(sns.barplot, \"gender\", \"churn_rate\", palette = \"Blues_d\", order= ['Female', 'Male'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 518.4x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97HQfSKUZbCP"
      },
      "source": [
        "Gender is not an indicative of churn.\n",
        "SeniorCitizens are only 16% of customers, but they have a much higher churn rate: 42% against 23% for non-senior customers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ha8FlmxwZbCP"
      },
      "source": [
        "##### Phone and Internet services\n",
        "There are only two features here: \n",
        "If the client has phone and if he has more than one line.\n",
        "Both can be summed up in one chart:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PqGh05BHZbCQ",
        "outputId": "952a664d-0912-4ee9-b0fd-0a5b42f3f4a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        }
      },
      "source": [
        "plt.figure(figsize=(9, 4.5))\n",
        "barplot_percentages(\"MultipleLines\", orient='h')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiBugZruZbCS"
      },
      "source": [
        "Customer with multiple lines have higher of churn rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l-0SEMENZbCS",
        "outputId": "86dd2c91-521d-4528-c393-6bf3c418dca4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        }
      },
      "source": [
        "plt.figure(figsize=(9, 4.5))\n",
        "barplot_percentages(\"InternetService\", orient=\"h\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 648x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2PNX4p1ZbCU"
      },
      "source": [
        "##### Additional service analysis\n",
        "\n",
        "The first plot shows the total number of customers for each additional service, while the second shows the number of clients that churn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyZrLaQQZbCV",
        "outputId": "449273ae-5287-418b-af34-b3458d0b22ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        }
      },
      "source": [
        "#There are six additional services for customers with internet:\n",
        "cols = [\"OnlineSecurity\", \"OnlineBackup\", \"DeviceProtection\", \"TechSupport\", \"StreamingTV\", \"StreamingMovies\"]\n",
        "df1 = pd.melt(df[df[\"InternetService\"] != \"No\"][cols]).rename({'value': 'Has service'}, axis=1)\n",
        "plt.figure(figsize=(10, 4.5))\n",
        "ax = sns.countplot(data=df1, x='variable', hue='Has service')\n",
        "ax.set(xlabel='Additional service', ylabel='Num of customers')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOH1cZYBZbCY",
        "outputId": "a44f68fc-73f6-430d-b2bf-50ee85efdf6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        }
      },
      "source": [
        "plt.figure(figsize=(10, 4.5))\n",
        "df1 = df[(df.InternetService != \"No\") & (df.Churn == \"Yes\")]\n",
        "df1 = pd.melt(df1[cols]).rename({'value': 'Has service'}, axis=1)\n",
        "ax = sns.countplot(data=df1, x='variable', hue='Has service', hue_order=['No', 'Yes'])\n",
        "ax.set(xlabel='Additional service', ylabel='Num of churns')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-jFhK6yZbCa"
      },
      "source": [
        "Customers with the OnlineSecurity, Backup,Protection and tech support are more unlikely to churn."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWq0lkSxZbCa"
      },
      "source": [
        "##### Contract and Payment\n",
        "\n",
        "Customers with paperless billing are more probable to churn.\n",
        "The preferred payment method is Electronic check with around 35% of customers. This also has a very high churn rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ovAp9RUZbCb",
        "outputId": "2dbd387f-56b7-483d-82e4-39672db5e224",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "g = sns.FacetGrid(df, col=\"PaperlessBilling\", height=4, aspect=.9)\n",
        "ax = g.map(sns.barplot, \"Contract\", \"churn_rate\", palette = \"Blues_d\", order= ['Month-to-month', 'One year', 'Two year'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 518.4x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOxFcFfXZbCc",
        "outputId": "a9dc296b-b15e-49a2-a52c-e4289216fd3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        }
      },
      "source": [
        "plt.figure(figsize=(9, 4.5))\n",
        "barplot_percentages(\"PaymentMethod\", orient='h')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "av7No08HZbCe"
      },
      "source": [
        "##### Correlation between features "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnKogIDDZbCe",
        "outputId": "ceb465f7-dae9-476d-d76e-e7bf0e6ee9e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "df.drop(['customerID', 'churn_rate'],\n",
        "        axis=1, inplace=True)\n",
        "corr = df.apply(lambda x: pd.factorize(x)[0]).corr()\n",
        "ax = sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, \n",
        "                 linewidths=.2, cmap=\"YlGnBu\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "md2o3EWfZbCh"
      },
      "source": [
        "### Pre-process to build model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTT8Z8sfZbCh",
        "outputId": "d56b28c4-af73-4271-92f3-546d24e57fd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.50</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   gender  SeniorCitizen Partner  ... MonthlyCharges  TotalCharges Churn\n",
              "0  Female              0     Yes  ...          29.85         29.85    No\n",
              "1    Male              0      No  ...          56.95       1889.50    No\n",
              "2    Male              0      No  ...          53.85        108.15   Yes\n",
              "3    Male              0      No  ...          42.30       1840.75    No\n",
              "4  Female              0      No  ...          70.70        151.65   Yes\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8g833xTcZbCj"
      },
      "source": [
        "#Let's print unique values in object columns to see data values\n",
        "def print_unique_col_values(df):\n",
        "       for column in df:\n",
        "            if df[column].dtypes=='object':\n",
        "                print(f'{column}: {df[column].unique()}')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DimOxyXoZbCl",
        "outputId": "4cd07e63-85ef-4672-e553-fdf33bbe3103",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "print_unique_col_values(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender: ['Female' 'Male']\n",
            "Partner: ['Yes' 'No']\n",
            "Dependents: ['No' 'Yes']\n",
            "PhoneService: ['No' 'Yes']\n",
            "MultipleLines: ['No phone service' 'No' 'Yes']\n",
            "InternetService: ['DSL' 'Fiber optic' 'No']\n",
            "OnlineSecurity: ['No' 'Yes' 'No internet service']\n",
            "OnlineBackup: ['Yes' 'No' 'No internet service']\n",
            "DeviceProtection: ['No' 'Yes' 'No internet service']\n",
            "TechSupport: ['No' 'Yes' 'No internet service']\n",
            "StreamingTV: ['No' 'Yes' 'No internet service']\n",
            "StreamingMovies: ['No' 'Yes' 'No internet service']\n",
            "Contract: ['Month-to-month' 'One year' 'Two year']\n",
            "PaperlessBilling: ['Yes' 'No']\n",
            "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
            " 'Credit card (automatic)']\n",
            "Churn: ['No' 'Yes']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUitiLhaZbCn"
      },
      "source": [
        "#Some of the columns have no internet service or no phone service, that can be replaced with a simple No\n",
        "df.replace('No internet service','No',inplace=True)\n",
        "df.replace('No phone service','No',inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cf96T73LZbCp",
        "outputId": "5ccb748e-62a3-4fe6-91f8-403f49228f3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "print_unique_col_values(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender: ['Female' 'Male']\n",
            "Partner: ['Yes' 'No']\n",
            "Dependents: ['No' 'Yes']\n",
            "PhoneService: ['No' 'Yes']\n",
            "MultipleLines: ['No' 'Yes']\n",
            "InternetService: ['DSL' 'Fiber optic' 'No']\n",
            "OnlineSecurity: ['No' 'Yes']\n",
            "OnlineBackup: ['Yes' 'No']\n",
            "DeviceProtection: ['No' 'Yes']\n",
            "TechSupport: ['No' 'Yes']\n",
            "StreamingTV: ['No' 'Yes']\n",
            "StreamingMovies: ['No' 'Yes']\n",
            "Contract: ['Month-to-month' 'One year' 'Two year']\n",
            "PaperlessBilling: ['Yes' 'No']\n",
            "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
            " 'Credit card (automatic)']\n",
            "Churn: ['No' 'Yes']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1R1pab2JZbCr",
        "outputId": "a1615f69-2e7a-47d6-dfeb-07622f5840f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        }
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.50</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7038</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>24</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>One year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>84.80</td>\n",
              "      <td>1990.50</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7039</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>72</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>One year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>103.20</td>\n",
              "      <td>7362.90</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7040</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.60</td>\n",
              "      <td>346.45</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7041</th>\n",
              "      <td>Male</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>4</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</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>74.40</td>\n",
              "      <td>306.60</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7042</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>66</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Two year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>105.65</td>\n",
              "      <td>6844.50</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7043 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      gender  SeniorCitizen Partner  ... MonthlyCharges  TotalCharges Churn\n",
              "0     Female              0     Yes  ...          29.85         29.85    No\n",
              "1       Male              0      No  ...          56.95       1889.50    No\n",
              "2       Male              0      No  ...          53.85        108.15   Yes\n",
              "3       Male              0      No  ...          42.30       1840.75    No\n",
              "4     Female              0      No  ...          70.70        151.65   Yes\n",
              "...      ...            ...     ...  ...            ...           ...   ...\n",
              "7038    Male              0     Yes  ...          84.80       1990.50    No\n",
              "7039  Female              0     Yes  ...         103.20       7362.90    No\n",
              "7040  Female              0     Yes  ...          29.60        346.45    No\n",
              "7041    Male              1     Yes  ...          74.40        306.60   Yes\n",
              "7042    Male              0      No  ...         105.65       6844.50    No\n",
              "\n",
              "[7043 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eiZ6pWsVZbCt"
      },
      "source": [
        "#Here, converting Yes= 1, No =0\n",
        "\n",
        "yes_no_columns = ['Partner','Dependents','PhoneService','MultipleLines','OnlineSecurity','OnlineBackup',\n",
        "                  'DeviceProtection','TechSupport','StreamingTV','StreamingMovies','PaperlessBilling','Churn']\n",
        "for col in yes_no_columns:\n",
        "    df[col].replace({'Yes': 1,'No': 0},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqngNZqQZbCu",
        "outputId": "adbecbf4-8859-4a82-d098-e2ffa124a939",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "for col in df:\n",
        "    print(f'{col}: {df[col].unique()}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender: ['Female' 'Male']\n",
            "SeniorCitizen: [0 1]\n",
            "Partner: [1 0]\n",
            "Dependents: [0 1]\n",
            "tenure: [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
            "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
            " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26  0\n",
            " 39]\n",
            "PhoneService: [0 1]\n",
            "MultipleLines: [0 1]\n",
            "InternetService: ['DSL' 'Fiber optic' 'No']\n",
            "OnlineSecurity: [0 1]\n",
            "OnlineBackup: [1 0]\n",
            "DeviceProtection: [0 1]\n",
            "TechSupport: [0 1]\n",
            "StreamingTV: [0 1]\n",
            "StreamingMovies: [0 1]\n",
            "Contract: ['Month-to-month' 'One year' 'Two year']\n",
            "PaperlessBilling: [1 0]\n",
            "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
            " 'Credit card (automatic)']\n",
            "MonthlyCharges: [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
            "TotalCharges: [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
            "Churn: [0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aH_M2nSVZbCw"
      },
      "source": [
        "\n",
        "df['gender'].replace({'Female':1,'Male':0},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0H_g7ZmZbCy",
        "outputId": "b1e8e53e-dcb9-484a-8022-d40c9c618584",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "\n",
        "df.gender.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "86E2nE-HZbC1"
      },
      "source": [
        "##### One hot encoding for categorical columns "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sSC4vWZMZbC1",
        "outputId": "92a00bbb-4e06-4a0a-bc3a-ecb34cb4ef9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "\n",
        "df1 = pd.get_dummies(data=df, columns=['InternetService','Contract','PaymentMethod'])\n",
        "df1.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',\n",
              "       'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup',\n",
              "       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
              "       'PaperlessBilling', 'MonthlyCharges', 'TotalCharges', 'Churn',\n",
              "       'InternetService_DSL', 'InternetService_Fiber optic',\n",
              "       'InternetService_No', 'Contract_Month-to-month', 'Contract_One year',\n",
              "       'Contract_Two year', 'PaymentMethod_Bank transfer (automatic)',\n",
              "       'PaymentMethod_Credit card (automatic)',\n",
              "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jka7EGVdZbC3",
        "outputId": "30de11a1-45a6-4241-e447-8ec6345e2c8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "df1.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>InternetService_No</th>\n",
              "      <th>Contract_Month-to-month</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</th>\n",
              "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
              "      <th>PaymentMethod_Credit card (automatic)</th>\n",
              "      <th>PaymentMethod_Electronic check</th>\n",
              "      <th>PaymentMethod_Mailed check</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6383</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40.1</td>\n",
              "      <td>857.75</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5006</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>55.7</td>\n",
              "      <td>899.80</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1189</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>85.7</td>\n",
              "      <td>256.75</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1956</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>72</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>101.3</td>\n",
              "      <td>7261.25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4435</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>80.5</td>\n",
              "      <td>1336.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      gender  ...  PaymentMethod_Mailed check\n",
              "6383       0  ...                           1\n",
              "5006       1  ...                           1\n",
              "1189       0  ...                           0\n",
              "1956       1  ...                           0\n",
              "4435       0  ...                           0\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YP8y2nq8ZbC5",
        "outputId": "df6408c8-0957-4636-f6c1-7ed2d2eff7ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        }
      },
      "source": [
        "\n",
        "df1.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender                                       int64\n",
              "SeniorCitizen                                int64\n",
              "Partner                                      int64\n",
              "Dependents                                   int64\n",
              "tenure                                       int64\n",
              "PhoneService                                 int64\n",
              "MultipleLines                                int64\n",
              "OnlineSecurity                               int64\n",
              "OnlineBackup                                 int64\n",
              "DeviceProtection                             int64\n",
              "TechSupport                                  int64\n",
              "StreamingTV                                  int64\n",
              "StreamingMovies                              int64\n",
              "PaperlessBilling                             int64\n",
              "MonthlyCharges                             float64\n",
              "TotalCharges                               float64\n",
              "Churn                                        int64\n",
              "InternetService_DSL                          uint8\n",
              "InternetService_Fiber optic                  uint8\n",
              "InternetService_No                           uint8\n",
              "Contract_Month-to-month                      uint8\n",
              "Contract_One year                            uint8\n",
              "Contract_Two year                            uint8\n",
              "PaymentMethod_Bank transfer (automatic)      uint8\n",
              "PaymentMethod_Credit card (automatic)        uint8\n",
              "PaymentMethod_Electronic check               uint8\n",
              "PaymentMethod_Mailed check                   uint8\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-M_xSmkZbC7"
      },
      "source": [
        "cols_to_scale = ['tenure','MonthlyCharges','TotalCharges']\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "df1[cols_to_scale] = scaler.fit_transform(df1[cols_to_scale])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UmjlB_MYZbC9",
        "outputId": "8efc319d-2ca7-409f-c544-3b993a27c109",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        }
      },
      "source": [
        "\n",
        "for col in df1:\n",
        "    print(f'{col}: {df1[col].unique()}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender: [1 0]\n",
            "SeniorCitizen: [0 1]\n",
            "Partner: [1 0]\n",
            "Dependents: [0 1]\n",
            "tenure: [0.01388889 0.47222222 0.02777778 0.625      0.11111111 0.30555556\n",
            " 0.13888889 0.38888889 0.86111111 0.18055556 0.22222222 0.80555556\n",
            " 0.68055556 0.34722222 0.95833333 0.72222222 0.98611111 0.29166667\n",
            " 0.16666667 0.41666667 0.65277778 1.         0.23611111 0.375\n",
            " 0.06944444 0.63888889 0.15277778 0.97222222 0.875      0.59722222\n",
            " 0.20833333 0.83333333 0.25       0.91666667 0.125      0.04166667\n",
            " 0.43055556 0.69444444 0.88888889 0.77777778 0.09722222 0.58333333\n",
            " 0.48611111 0.66666667 0.40277778 0.90277778 0.52777778 0.94444444\n",
            " 0.44444444 0.76388889 0.51388889 0.5        0.56944444 0.08333333\n",
            " 0.05555556 0.45833333 0.93055556 0.31944444 0.79166667 0.84722222\n",
            " 0.19444444 0.27777778 0.73611111 0.55555556 0.81944444 0.33333333\n",
            " 0.61111111 0.26388889 0.75       0.70833333 0.36111111 0.\n",
            " 0.54166667]\n",
            "PhoneService: [0 1]\n",
            "MultipleLines: [0 1]\n",
            "OnlineSecurity: [0 1]\n",
            "OnlineBackup: [1 0]\n",
            "DeviceProtection: [0 1]\n",
            "TechSupport: [0 1]\n",
            "StreamingTV: [0 1]\n",
            "StreamingMovies: [0 1]\n",
            "PaperlessBilling: [1 0]\n",
            "MonthlyCharges: [0.11542289 0.38507463 0.35422886 ... 0.44626866 0.25820896 0.60149254]\n",
            "TotalCharges: [0.00343704 0.21756402 0.01245279 ... 0.03989153 0.03530306 0.78810105]\n",
            "Churn: [0 1]\n",
            "InternetService_DSL: [1 0]\n",
            "InternetService_Fiber optic: [0 1]\n",
            "InternetService_No: [0 1]\n",
            "Contract_Month-to-month: [1 0]\n",
            "Contract_One year: [0 1]\n",
            "Contract_Two year: [0 1]\n",
            "PaymentMethod_Bank transfer (automatic): [0 1]\n",
            "PaymentMethod_Credit card (automatic): [0 1]\n",
            "PaymentMethod_Electronic check: [1 0]\n",
            "PaymentMethod_Mailed check: [0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Vn3CnxUZbC_"
      },
      "source": [
        "##### Train test split "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUEhErqDZbC_"
      },
      "source": [
        "X = df1.drop('Churn',axis='columns')\n",
        "y = df1['Churn']\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVoTTAH5ZbDB",
        "outputId": "67968e40-e9f6-4e25-e841-47c308cf2636",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "\n",
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5634, 26)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k58L0x1lZbDC",
        "outputId": "f8e76943-770c-49f8-c102-6468f2e5f49a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1409, 26)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cs1zBvA3ZbDE",
        "outputId": "ff5d950d-4624-4054-d3cd-1bcd136e227f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "X_train[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>InternetService_No</th>\n",
              "      <th>Contract_Month-to-month</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</th>\n",
              "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
              "      <th>PaymentMethod_Credit card (automatic)</th>\n",
              "      <th>PaymentMethod_Electronic check</th>\n",
              "      <th>PaymentMethod_Mailed check</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5860</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.027778</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.514925</td>\n",
              "      <td>0.017623</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2458</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.694444</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.863682</td>\n",
              "      <td>0.594521</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5879</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458333</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.425871</td>\n",
              "      <td>0.232406</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4708</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.777778</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.428358</td>\n",
              "      <td>0.385363</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1293</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.930556</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.924876</td>\n",
              "      <td>0.867124</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2242</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.611111</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.065672</td>\n",
              "      <td>0.116710</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1444</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.569444</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.924378</td>\n",
              "      <td>0.518970</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3269</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.902778</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.065174</td>\n",
              "      <td>0.184339</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.013889</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.011940</td>\n",
              "      <td>0.002240</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4191</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.900498</td>\n",
              "      <td>0.791233</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      gender  ...  PaymentMethod_Mailed check\n",
              "5860       1  ...                           0\n",
              "2458       0  ...                           0\n",
              "5879       0  ...                           1\n",
              "4708       1  ...                           0\n",
              "1293       0  ...                           0\n",
              "2242       0  ...                           0\n",
              "1444       0  ...                           0\n",
              "3269       0  ...                           0\n",
              "101        1  ...                           0\n",
              "4191       1  ...                           0\n",
              "\n",
              "[10 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDv1Yn1uZbDH",
        "outputId": "c0eef8f3-49b1-4538-cce3-e128c71e3bcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(X_train.columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQDnWKSlZbDK"
      },
      "source": [
        "### Build a model (ANN) in tensorflow/keras "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IcNL_abHZbDK",
        "outputId": "699f2cee-ef96-4e04-e604-d1eca6fccee0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(26, input_shape=(26,), activation='relu'),\n",
        "    keras.layers.Dense(15, activation='relu'),\n",
        "    keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "# opt = keras.optimizers.Adam(learning_rate=0.01)\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train, y_train, epochs=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.5035 - accuracy: 0.7474\n",
            "Epoch 2/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4302 - accuracy: 0.7930\n",
            "Epoch 3/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4229 - accuracy: 0.7980\n",
            "Epoch 4/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4181 - accuracy: 0.8055\n",
            "Epoch 5/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4145 - accuracy: 0.8067\n",
            "Epoch 6/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4132 - accuracy: 0.8046\n",
            "Epoch 7/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4118 - accuracy: 0.8090\n",
            "Epoch 8/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4093 - accuracy: 0.8080\n",
            "Epoch 9/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4086 - accuracy: 0.8094\n",
            "Epoch 10/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4080 - accuracy: 0.8083\n",
            "Epoch 11/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4061 - accuracy: 0.8080\n",
            "Epoch 12/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4037 - accuracy: 0.8126\n",
            "Epoch 13/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4040 - accuracy: 0.8131\n",
            "Epoch 14/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4034 - accuracy: 0.8115\n",
            "Epoch 15/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4013 - accuracy: 0.8131\n",
            "Epoch 16/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4001 - accuracy: 0.8168\n",
            "Epoch 17/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.4010 - accuracy: 0.8131\n",
            "Epoch 18/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3993 - accuracy: 0.8154\n",
            "Epoch 19/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3996 - accuracy: 0.8156\n",
            "Epoch 20/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3985 - accuracy: 0.8165\n",
            "Epoch 21/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3971 - accuracy: 0.8175\n",
            "Epoch 22/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3961 - accuracy: 0.8181\n",
            "Epoch 23/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3952 - accuracy: 0.8154\n",
            "Epoch 24/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3941 - accuracy: 0.8175\n",
            "Epoch 25/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3955 - accuracy: 0.8175\n",
            "Epoch 26/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3925 - accuracy: 0.8197\n",
            "Epoch 27/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3931 - accuracy: 0.8170\n",
            "Epoch 28/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3924 - accuracy: 0.8179\n",
            "Epoch 29/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3910 - accuracy: 0.8172\n",
            "Epoch 30/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3905 - accuracy: 0.8193\n",
            "Epoch 31/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3909 - accuracy: 0.8206\n",
            "Epoch 32/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3895 - accuracy: 0.8195\n",
            "Epoch 33/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3878 - accuracy: 0.8181\n",
            "Epoch 34/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3887 - accuracy: 0.8179\n",
            "Epoch 35/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3874 - accuracy: 0.8202\n",
            "Epoch 36/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3864 - accuracy: 0.8191\n",
            "Epoch 37/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3863 - accuracy: 0.8200\n",
            "Epoch 38/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3862 - accuracy: 0.8181\n",
            "Epoch 39/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3860 - accuracy: 0.8179\n",
            "Epoch 40/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3858 - accuracy: 0.8211\n",
            "Epoch 41/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3836 - accuracy: 0.8200\n",
            "Epoch 42/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3842 - accuracy: 0.8223\n",
            "Epoch 43/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3827 - accuracy: 0.8204\n",
            "Epoch 44/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3831 - accuracy: 0.8220\n",
            "Epoch 45/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3831 - accuracy: 0.8222\n",
            "Epoch 46/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3817 - accuracy: 0.8198\n",
            "Epoch 47/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3817 - accuracy: 0.8214\n",
            "Epoch 48/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3812 - accuracy: 0.8222\n",
            "Epoch 49/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3809 - accuracy: 0.8227\n",
            "Epoch 50/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3799 - accuracy: 0.8211\n",
            "Epoch 51/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3797 - accuracy: 0.8218\n",
            "Epoch 52/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3796 - accuracy: 0.8220\n",
            "Epoch 53/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3788 - accuracy: 0.8232\n",
            "Epoch 54/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3785 - accuracy: 0.8246\n",
            "Epoch 55/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3776 - accuracy: 0.8211\n",
            "Epoch 56/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3783 - accuracy: 0.8236\n",
            "Epoch 57/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3771 - accuracy: 0.8245\n",
            "Epoch 58/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3762 - accuracy: 0.8220\n",
            "Epoch 59/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3757 - accuracy: 0.8248\n",
            "Epoch 60/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3759 - accuracy: 0.8237\n",
            "Epoch 61/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3750 - accuracy: 0.8268\n",
            "Epoch 62/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3752 - accuracy: 0.8243\n",
            "Epoch 63/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3758 - accuracy: 0.8255\n",
            "Epoch 64/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3735 - accuracy: 0.8250\n",
            "Epoch 65/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3732 - accuracy: 0.8271\n",
            "Epoch 66/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3738 - accuracy: 0.8248\n",
            "Epoch 67/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3735 - accuracy: 0.8229\n",
            "Epoch 68/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3715 - accuracy: 0.8236\n",
            "Epoch 69/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3713 - accuracy: 0.8241\n",
            "Epoch 70/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3718 - accuracy: 0.8252\n",
            "Epoch 71/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3720 - accuracy: 0.8259\n",
            "Epoch 72/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3714 - accuracy: 0.8241\n",
            "Epoch 73/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3712 - accuracy: 0.8264\n",
            "Epoch 74/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3703 - accuracy: 0.8262\n",
            "Epoch 75/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3696 - accuracy: 0.8230\n",
            "Epoch 76/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3690 - accuracy: 0.8282\n",
            "Epoch 77/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3691 - accuracy: 0.8285\n",
            "Epoch 78/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3691 - accuracy: 0.8252\n",
            "Epoch 79/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3690 - accuracy: 0.8259\n",
            "Epoch 80/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3676 - accuracy: 0.8259\n",
            "Epoch 81/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3681 - accuracy: 0.8239\n",
            "Epoch 82/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3675 - accuracy: 0.8296\n",
            "Epoch 83/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3661 - accuracy: 0.8236\n",
            "Epoch 84/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3651 - accuracy: 0.8278\n",
            "Epoch 85/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3655 - accuracy: 0.8273\n",
            "Epoch 86/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3653 - accuracy: 0.8259\n",
            "Epoch 87/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3651 - accuracy: 0.8278\n",
            "Epoch 88/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3653 - accuracy: 0.8241\n",
            "Epoch 89/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3637 - accuracy: 0.8289\n",
            "Epoch 90/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3647 - accuracy: 0.8284\n",
            "Epoch 91/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3630 - accuracy: 0.8294\n",
            "Epoch 92/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3647 - accuracy: 0.8271\n",
            "Epoch 93/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3635 - accuracy: 0.8246\n",
            "Epoch 94/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3623 - accuracy: 0.8269\n",
            "Epoch 95/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3625 - accuracy: 0.8271\n",
            "Epoch 96/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3611 - accuracy: 0.8277\n",
            "Epoch 97/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3608 - accuracy: 0.8291\n",
            "Epoch 98/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3611 - accuracy: 0.8300\n",
            "Epoch 99/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3616 - accuracy: 0.8280\n",
            "Epoch 100/100\n",
            "177/177 [==============================] - 0s 1ms/step - loss: 0.3607 - accuracy: 0.8285\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f877bfcde80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjWl8PW3ZbDM",
        "outputId": "bc7e1625-2d84-4249-8b0a-0acd1e7a6a7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "45/45 [==============================] - 0s 949us/step - loss: 0.4588 - accuracy: 0.7779\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.458802729845047, 0.7778566479682922]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWiv47jBZbDO",
        "outputId": "4b9942cf-e42a-434a-d085-644b10fecab1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "yp = model.predict(X_test)\n",
        "yp[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.36123043],\n",
              "       [0.50069755],\n",
              "       [0.32254955],\n",
              "       [0.76979315],\n",
              "       [0.09441373]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBtyUbFDaRX7"
      },
      "source": [
        "y_pred = []\n",
        "for element in yp:\n",
        "    if element > 0.5:\n",
        "        y_pred.append(1)\n",
        "    else:\n",
        "        y_pred.append(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMASJfncaUAB",
        "outputId": "0c41f772-8e77-4fa7-b28c-20f30fb0aa27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "y_pred[:10]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 0, 1, 0, 1, 1, 1, 0, 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzQYDU8oaXGT",
        "outputId": "1c8ebf60-ac58-4791-ddba-8875430295fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "y_test[:10]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4213    1\n",
              "5035    0\n",
              "3713    1\n",
              "1720    0\n",
              "234     0\n",
              "4558    1\n",
              "40      0\n",
              "3455    1\n",
              "5944    1\n",
              "1089    0\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xL_vPSawadhC"
      },
      "source": [
        "##### Confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7FJ1aTKaZM8",
        "outputId": "32d72980-c6a8-45b6-9d6d-5eef19ea1d5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix , classification_report\n",
        "\n",
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.86      0.85      1023\n",
            "           1       0.60      0.57      0.59       386\n",
            "\n",
            "    accuracy                           0.78      1409\n",
            "   macro avg       0.72      0.71      0.72      1409\n",
            "weighted avg       0.77      0.78      0.78      1409\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "juwYYLR8ahxo",
        "outputId": "de3ca676-2693-4447-f9d2-55cb4e748cd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        }
      },
      "source": [
        "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_pred)\n",
        "\n",
        "plt.figure(figsize = (10,7))\n",
        "sns.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JUp+rxRyazPwlQER8JTNP734uIr4C/LLJY5MkSdokmzKH5tgNtB3X6IFIkqS+lRkN21qtpzk0pwIfBnaPiLndTg0G/qvZA5MkSc1Vlzk0PwB+BvwbcEa39qczc1lTRyVJkrQZeppDsxJYGRGnr3dqUEQMyszfN3Ng249+UzMvL2kjXrPDiFYPQVIfqcWk4G5+Stfy7QC2AXYD7gf+rInjkiRJTdYOc18apdeAJjP/vPtxROxH19waSZKktrDZb9vOzNkRcVAzBiNJkvpOX5acIuJh4Gm63gu5OjMPiIihwBV0vQD7YeC9mbk8IgI4HzgeeBb4YGbO7un6m/Jyyk92O3wFsB/w35v9TSRJUltpwSKnN2XmU92OzwCmZ+bZEXFGcXw6XY+HGVdsBwHfKn5u1KY8h2Zwt21ruubUTNjcbyBJktpLZ0bDti00AZhc7E8GTujWPiW7zACGRESPKxZ6zNBERD9gcGZ+aktHKkmSRFdCaFpEJPDtzJwEDM/MR4vzjwHDi/2RwCPdPru4aHuUjejpwXr9M3N1RBz2ckYvSZLaUyNXOUVEB9DRrWlSEbSsdXhmLomIXYAbI+K3Lx1LZhHsbJGeMjR30DVfZk5ETAV+BDzT7cZXb+lNJUlS63U28FpF8DKph/NLip9PRMQ1wIHA4xExIjMfLUpKTxTdlwCju318VNG2UZsyh2YbYClwFPA24O3FT0mSpF5FxMCIGLx2H3gzMA+YCkwsuk0Eri32pwInRZeDgZXdSlMb1FOGZpdihdM8/vhgvbUq9PYHSZLqKemzZdvDgWu6VmPTH/hBZt4QEb8BroyIU4BFwHuL/tfTtWR7AV3Ltk/u7QY9BTT9gEGwwW9rQCNJUsl19tFv88xcCOyzgfalwNEbaE/gI5tzj54Cmkcz8wubczFJkqRW6Cmgqc4LHiRJ0p/orNCv+p4Cmj9JAUmSpOrowzk0TbfRVU6ZuawvByJJkrSlNvvllJIkqRoa+RyaVjOgkSSppmpRcpIkSSoLMzSSJNWUJSdJklR6VQpoLDlJkqTSM0MjSVJNVWlSsAGNJEk11VmdeMaSkyRJKj8zNJIk1VRd3uUkSZIqLFs9gAay5CRJkkrPDI0kSTVVpefQGNBIklRTnVGdOTSWnCRJUumZoZEkqaaqNCnYgEaSpJqq0hwaS06SJKn0zNBIklRTVXr1gQGNJEk1VaUnBVtykiRJpWeGRpKkmnKVkyRJKr0qzaGx5CRJkkrPDI0kSTVVpefQGNBIklRTVZpDY8lJkiSVnhkaSZJqqkqTgg1oJEmqqSrNobHkJEmSSs8MjSRJNVWlDI0BjSRJNZUVmkNjyUmSJJWeGRpJkmrKkpMkSSq9KgU0lpwkSVLpmaGRJKmmqvTqAwMaSZJqqkpPCrbkJEmSSs8MjSRJNeWkYEmSVHqdDdw2RUT0i4i7IuI/i+PdImJmRCyIiCsiYkDRvnVxvKA4P6a3axvQSJKkvvKPwH3djr8CnJeZY4HlwClF+ynA8qL9vKJfjwxoJEmqqWzg1puIGAW8Fbi4OA7gKOCqostk4IRif0JxTHH+6KL/RhnQSJJUU53RuC0iOiJiVretY73bfR34Z/5YodoJWJGZq4vjxcDIYn8k8AhAcX5l0X+jnBQsSVJNNXJScGZOAiZt6FxEvA14IjPvjIg3NvC26xjQSJKkZjsMeEdEHA9sA2wPnA8MiYj+RRZmFLCk6L8EGA0sjoj+wA7A0p5uYMlJkqSa6qs5NJn56cwclZljgBOBmzLzA8DNwLuLbhOBa4v9qcUxxfmbMrPH25ihkSSppjpb//KD04EfRsRZwF3AJUX7JcBlEbEAWEZXENQjAxpJktRnMvMW4JZifyFw4Ab6PA+8Z3Oua0AjSVJNVelJwQY0kiTVVMsLTg3kpGBJklR6ZmgkSaopS06SJKn0Ont8mUC5WHKSJEmlZ4ZGkqSaaoPn0DSMAY0kSTVVnXDGkpMkSaoAMzSSJNWUq5wkSVLpVWkOjSUnSZJUemZoJEmqqerkZwxoJEmqrSrNobHkJEmSSs8MjSRJNVWlScEGNJIk1VR1whlLTpIkqQLM0EiSVFNVmhRsQCNJUk1lhYpOlpwkSVLpmaGRJKmmLDlJkqTSq9KybUtOkiSp9MzQSJJUU9XJzxjQSJJUW5acJEmS2ogBjTbbRRedw6JFdzJr1rSXtJ966geZM2c6d955I1/60qcBeNWrRrFs2f3MmHE9M2ZczwUXfKkVQ5Yq4ZW7DmfKNRdx/W1X8tNfXcFJHScC8M+f/xg3/NdVTL3lcr753XMYvP0gAIbsuANTrrmIux6+lc+d/c+tHLraVGcDt1az5KTNdtllP+KiiyZz8cVfW9c2fvwhvO1tx3LggcexatUqdt55p3XnFi5cxMEHH9+KoUqVsmbNas7+/HnMn3s/Awdux9XTL+P2W2Zy+y9n8tWzvsmaNWv41GdP4+//8WTO/eI3eOGFFzj/7G8xbq+x7PHa3Vs9fLUhH6ynWrv99jtYtmzFS9o6Ov6ac8+9kFWrVgHw5JNLWzE0qdKefHwp8+feD8AzzzzLgw88zPARu3D7LTNZs2YNAHffeQ+v3HUXAJ579nnunHk3L7zwQsvGLPWVPg9oIuLkvr6nmm/s2N047LADufXWnzBt2hXsv/9frDs3Zsxofv3r65k27QoOO+wNLRylVB0jR49g7z/fk7vvnPeS9ne9/x3cOv2/WjQqlY0lp5fnTODSDZ2IiA6gA6B//6H07z+oL8ell6F///4MHTqE8eNP4IAD9uF737uQ1772cB577An22OMQli1bwetf/zquvPI77LffsTz99B9aPWSptLYbuC3fuPTf+fK/fJVn/vDMuvZ/+MTfsmb1GqZe9bMWjk5lUqWSU1MCmoiYu7FTwPCNfS4zJwGTALbd9tXV+bdcA0uWPMpPfnIDALNm3U1nZyfDhg3lqaeWsWxZVxnqrrvmsXDhIsaN243Zs+9p5XCl0urfvx/fuPTfue6qG5j205vXtf/liW/jTccezsR3ndrC0Umt06wMzXDg/wDL12sPwFxoBV133TSOPPIQbr3114wduxsDBmzFU08tY9iwoSxbtoLOzk7GjBnN2LG78dBDv2/1cKXS+vLXP8eDDzzEpRd9f13bEUcdwt999CQ+MKGD559zvow2XTuUihqlWQHNfwKDMnPO+ici4pYm3VN9ZPLkCzjiiEMYNmxHFiyYwRe/eB6TJ1/Jt799DrNmTWPVqhf50If+CYDDDz+Iz372k7z44ot0diannfYZli9f2eJvIJXT/gftwwl/9VZ+e+/vuPbmroDma1+6kH/58qcYMGArvnvVNwGYM2sen/+//wbATXdOZdDggWw1YCuOOe5ITn7PR3nwgYda9h3UXjqzOsWQyDb9MpacpNYYPWjnVg9Bqq0HnpwVfXm/v3n1Oxv2u/ayRVf36djX53NoJEmqqSplDgxoJEmqKd/lJEmS1EbM0EiSVFM+h0aSJJVelZZtW3KSJEmlZ4ZGkqSaclKwJEkqvWzgPz2JiG0i4o6IuDsi7o2IM4v23SJiZkQsiIgrImJA0b51cbygOD+mt+9iQCNJkprtBeCozNwH2Bd4S0QcDHwFOC8zx9L1uqRTiv6nAMuL9vOKfj0yoJEkqaY6G7j1JLv8oTjcqtgSOAq4qmifDJxQ7E8ojinOHx0RPT6J2IBGkqSaysyGbRHRERGzum0d3e8VEf0iYg7wBHAj8CCwIjNXF10WAyOL/ZHAI8UYVwMrgZ16+i5OCpYkSS9bZk4CJvVwfg2wb0QMAa4B9mrk/Q1oJEmqqVascsrMFRFxM3AIMCQi+hdZmFHAkqLbEmA0sDgi+gM7AEt7uq4lJ0mSaqqv5tBExM5FZoaI2BY4FrgPuBl4d9FtInBtsT+1OKY4f1Nm9hh9maGRJKmm+vDVByOAyRHRj65kypWZ+Z8RMR/4YUScBdwFXFL0vwS4LCIWAMuAE3u7gQGNJElqqsycC7x+A+0LgQM30P488J7NuYcBjSRJNVWlJwUb0EiSVFO9TEspFScFS5Kk0jNDI0lSTfW2OqlMDGgkSaqpPlzl1HSWnCRJUumZoZEkqaZc5SRJkkrPVU6SJEltxAyNJEk1ZclJkiSVnqucJEmS2ogZGkmSaqqzQpOCDWgkSaqp6oQzlpwkSVIFmKGRJKmmXOUkSZJKr0oBjSUnSZJUemZoJEmqqSq9+sCARpKkmrLkJEmS1EbM0EiSVFNVevWBAY0kSTVVpTk0lpwkSVLpmaGRJKmmqjQp2IBGkqSasuQkSZLURszQSJJUU5acJElS6VVp2bYlJ0mSVHpmaCRJqqnOCk0KNqCRJKmmLDlJkiS1ETM0kiTVlCUnSZJUepacJEmS2ogZGkmSasqSkyRJKj1LTpIkSW3EDI0kSTVlyUmSJJWeJSdJkqQ2YoZGkqSayuxs9RAaxoBGkqSa6rTkJEmStGkiYnRE3BwR8yPi3oj4x6J9aETcGBG/K37uWLRHRFwQEQsiYm5E7NfbPQxoJEmqqcxs2NaL1cA/ZebewMHARyJib+AMYHpmjgOmF8cAxwHjiq0D+FZvNzCgkSSppjrJhm09ycxHM3N2sf80cB8wEpgATC66TQZOKPYnAFOyywxgSESM6OkeBjSSJOlli4iOiJjVbevYSL8xwOuBmcDwzHy0OPUYMLzYHwk80u1ji4u2jXJSsCRJNbUJpaLNudYkYFJPfSJiEPBj4OOZ+T8R0f3zGRFbPCADGkmSaqovnxQcEVvRFcx8PzOvLpofj4gRmfloUVJ6omhfAozu9vFRRdtGWXKSJElNFV2pmEuA+zLza91OTQUmFvsTgWu7tZ9UrHY6GFjZrTS1QWZoJEmqqT589cFhwN8A90TEnKLtM8DZwJURcQqwCHhvce564HhgAfAscHJvNzCgkSSppho5h6aX+9wGxEZOH72B/gl8ZHPuYUAjSVJN+aRgSZKkNmKGRpKkmuqrklNfMKCRJKmm+nLZdrNZcpIkSaVnhkaSpJqy5CRJkkrPVU6SJEltxAyNJEk1ZclJkiSVnqucJEmS2ogZGkmSaqoPX07ZdAY0kiTVlCUnSZKkNmKGRpKkmnKVkyRJKr0qzaGx5CRJkkrPDI0kSTVlyUmSJJVelQIaS06SJKn0zNBIklRT1cnPQFQp3aT2EREdmTmp1eOQ6sY/e6orS05qlo5WD0CqKf/sqZYMaCRJUukZ0EiSpNIzoFGzWMOXWsM/e6olJwVLkqTSM0MjSZJKz4BGkiSVngGNGioi3hIR90fEgog4o9XjkeoiIv4jIp6IiHmtHovUCgY0apiI6Ad8EzgO2Bt4X0Ts3dpRSbXxXeAtrR6E1CoGNGqkA4EFmbkwM1cBPwQmtHhMUi1k5q3AslaPQ2oVAxo10kjgkW7Hi4s2SZKayoBGkiSVngGNGmkJMLrb8aiiTZKkpjKgUSP9BhgXEbtFxADgRGBqi8ckSaoBAxo1TGauBj4K/By4D7gyM+9t7aikeoiIy4FfA3tGxOKIOKXVY5L6kq8+kCRJpWeGRpIklZ4BjSRJKj0DGkmSVHoGNJIkqfQMaCRJUukZ0EglFRFrImJORMyLiB9FxHYv41rfjYh3F/sX9/RS0Yh4Y0QcugX3eDgihm3pGCWpJwY0Unk9l5n7ZubrgFXAP3Q/GRH9t+SimfmhzJzfQ5c3Apsd0EhSMxnQSNXwK2BskT35VURMBeZHRL+IOCcifhMRcyPi7wGiy/+PiPsj4hfALmsvFBG3RMQBxf5bImJ2RNwdEdMjYgxdgdMniuzQERGxc0T8uLjHbyLisOKzO0XEtIi4NyIuBqJv/5VIqpMt+hucpPZRZGKOA24omvYDXpeZD0VEB7AyM98QEVsDt0fENOD1wJ7A3sBwYD7wH+tdd2fgO8D44lpDM3NZRFwE/CEzzy36/QA4LzNvi4hX0fWk6NcCnwduy8wvRMRbAZ9cK6lpDGik8to2IuYU+78CLqGrFHRHZj5UtL8Z+Iu182OAHYBxwHjg8sxcA/x3RNy0gesfDNy69lqZuWwj4zgG2DtiXQJm+4gYVNzjncVnfxoRy7fwe0pSrwxopPJ6LjP37d5QBBXPdG8CTsvMn6/X7/gGjuMVwMGZ+cIbvWEAAADQSURBVPwGxiJJfcI5NFK1/Rw4NSK2AoiIPSJiIHAr8FfFHJsRwJs28NkZwPiI2K347NCi/WlgcLd+04DT1h5ExNog61bg/UXbccCODftWkrQeAxqp2i6ma37M7IiYB3ybrszsNcDvinNT6HpL80tk5pNAB3B1RNwNXFGcug74y7WTgoGPAQcUk47n88fVVmfSFRDdS1fp6fdN+o6S5Nu2JUlS+ZmhkSRJpWdAI0mSSs+ARpIklZ4BjSRJKj0DGkmSVHoGNJIkqfQMaCRJUun9L+wdujyGoWEcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jwlNPxz5atlw",
        "outputId": "715cdc2f-2b75-45b2-be47-6093313603f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "y_test.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1409,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPSHnQbcbAKd"
      },
      "source": [
        "###### Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NK4jxTdca3Q6",
        "outputId": "6eea04e7-88c3-481b-c42a-c53ed7be21a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "round((862+229)/(862+229+137+179),2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.78"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FUIZZtmbck1b"
      },
      "source": [
        "###### Precision for 0 class. i.e. Precision for customers who did not churn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ByAs5RolbE2k",
        "outputId": "8c94dd87-dd79-4147-e9a6-76aa0fb25293",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "\n",
        "round(862/(862+179),2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.83"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cNLfATL-cq2K"
      },
      "source": [
        "###### Precision for 1 class. i.e. Precision for customers who actually churned"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoGTf87HcoD4",
        "outputId": "da0d9fd7-3ebd-4864-a371-0159c3597ebe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "round(229/(229+137),2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.63"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TIlYaqWtc3XC"
      },
      "source": [
        "###### Recall for 0 class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ps7OJPoAcvd8",
        "outputId": "ccb00353-dae2-4e55-eb0c-c9cd457336a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "round(862/(862+137),2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.86"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bb-Nb3KHc7w9",
        "outputId": "43408fdc-eb83-4a32-b9e9-2a2e9ce992b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "round(229/(229+179),2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.56"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eCNkRq7c-y0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}