TelcoCustomerChurn.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sciederrick/Data-Science-For-EveryOne/blob/main/TelcoCustomerChurn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
      "execution_count": 302,
      "metadata": {
        "id": "-lhJc_K-MMLF"
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      "source": [
        "# Google drive file id for the data set\n",
        "file_id=\"1s_6EKj_e3NTRBx45TvPfzWPwr0DO_LZE\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import warnings"
      ],
      "metadata": {
        "id": "ugf3V7wFSLma"
      },
      "execution_count": 303,
      "outputs": []
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      "source": [
        "# Suppress all warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "url = 'https://drive.google.com/uc?id={}'.format(file_id)\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "# The following lines adjust the granularity of reporting.\n",
        "pd.options.display.max_rows = 10\n",
        "pd.options.display.float_format = \"{:.1f}\".format\n",
        "\n",
        "# Has your data been loaded correctly (Does the number of rows and columns match your expectation?)\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
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        "outputId": "5a33f087-0594-4efc-ca52-22464e6013bf"
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      "execution_count": 304,
      "outputs": [
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          "data": {
            "text/plain": [
              "(7043, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 304
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    },
    {
      "cell_type": "markdown",
      "source": [
        "---------------------------------\n",
        "*Conduct Data Quality Checks*\n",
        "---------------------------------"
      ],
      "metadata": {
        "id": "BF1StQ29lXCI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assess data through static analysis\n",
        "df.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "4A8USjrFPhmn",
        "outputId": "f5541d54-4ec9-4212-d96f-29a25bf754c5"
      },
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
              "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
              "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
              "3  7795-CFOCW    Male              0      No         No      45           No   \n",
              "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
              "0  No phone service             DSL             No  ...               No   \n",
              "1                No             DSL            Yes  ...              Yes   \n",
              "2                No             DSL            Yes  ...               No   \n",
              "3  No phone service             DSL            Yes  ...              Yes   \n",
              "4                No     Fiber optic             No  ...               No   \n",
              "\n",
              "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
              "0          No          No              No  Month-to-month              Yes   \n",
              "1          No          No              No        One year               No   \n",
              "2          No          No              No  Month-to-month              Yes   \n",
              "3         Yes          No              No        One year               No   \n",
              "4          No          No              No  Month-to-month              Yes   \n",
              "\n",
              "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
              "0           Electronic check           29.9         29.85    No  \n",
              "1               Mailed check           57.0        1889.5    No  \n",
              "2               Mailed check           53.9        108.15   Yes  \n",
              "3  Bank transfer (automatic)           42.3       1840.75    No  \n",
              "4           Electronic check           70.7        151.65   Yes  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
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              "      <td>DSL</td>\n",
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              "      <td>No</td>\n",
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              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
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              "      <td>108.15</td>\n",
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          "metadata": {},
          "execution_count": 305
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    {
      "cell_type": "code",
      "source": [
        "# Assess data columns for Validity (right format/dtype), Completeness (non-null)\n",
        "df.info()\n",
        "# Why is total charges of type object (expect type float64)??"
      ],
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      "execution_count": 306,
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 21 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   customerID        7043 non-null   object \n",
            " 1   gender            7043 non-null   object \n",
            " 2   SeniorCitizen     7043 non-null   int64  \n",
            " 3   Partner           7043 non-null   object \n",
            " 4   Dependents        7043 non-null   object \n",
            " 5   tenure            7043 non-null   int64  \n",
            " 6   PhoneService      7043 non-null   object \n",
            " 7   MultipleLines     7043 non-null   object \n",
            " 8   InternetService   7043 non-null   object \n",
            " 9   OnlineSecurity    7043 non-null   object \n",
            " 10  OnlineBackup      7043 non-null   object \n",
            " 11  DeviceProtection  7043 non-null   object \n",
            " 12  TechSupport       7043 non-null   object \n",
            " 13  StreamingTV       7043 non-null   object \n",
            " 14  StreamingMovies   7043 non-null   object \n",
            " 15  Contract          7043 non-null   object \n",
            " 16  PaperlessBilling  7043 non-null   object \n",
            " 17  PaymentMethod     7043 non-null   object \n",
            " 18  MonthlyCharges    7043 non-null   float64\n",
            " 19  TotalCharges      7043 non-null   object \n",
            " 20  Churn             7043 non-null   object \n",
            "dtypes: float64(1), int64(2), object(18)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\t# Check for missing values (NAN, None) alternative `df.isnull()`\n",
        "\tdf.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xoETT-yMQ15g",
        "outputId": "2d5ca225-60a3-4b22-a6c7-324486de593c"
      },
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID          0\n",
              "gender              0\n",
              "SeniorCitizen       0\n",
              "Partner             0\n",
              "Dependents          0\n",
              "                   ..\n",
              "PaperlessBilling    0\n",
              "PaymentMethod       0\n",
              "MonthlyCharges      0\n",
              "TotalCharges        0\n",
              "Churn               0\n",
              "Length: 21, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\t# Check for duplicates\n",
        "\tdf.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VnwDG6nuiOwv",
        "outputId": "36842db0-c3ae-415b-97e1-031c7705f7b2"
      },
      "execution_count": 308,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 308
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\t# Understand distribution of your data, outliers, magnitude of numeric data based on statistical summary\n",
        "  # Action: deal with outliers, determine if you need to scale your data (e.g.,one feature has a mean in the thousands while another has a mean in the single digits)\n",
        "\tdf.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "DCqGHsWtiWYQ",
        "outputId": "ce50d7c0-6030-4561-9ee4-a92376c21225"
      },
      "execution_count": 309,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SeniorCitizen  tenure  MonthlyCharges\n",
              "count         7043.0  7043.0          7043.0\n",
              "mean             0.2    32.4            64.8\n",
              "std              0.4    24.6            30.1\n",
              "min              0.0     0.0            18.2\n",
              "25%              0.0     9.0            35.5\n",
              "50%              0.0    29.0            70.3\n",
              "75%              0.0    55.0            89.8\n",
              "max              1.0    72.0           118.8"
            ],
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              "      <th></th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>tenure</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>count</th>\n",
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              "      <td>7043.0</td>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 309
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "-------------------------------------------------------------\n",
        "*Data Cleaning*\n",
        "-------------------------------------------------------------\n",
        "* `df['TotalCharges']` has the wrong type `object`, convert to expected type\n",
        "`float`\n",
        "* Inconsistent casing on `tenure`, `gender`, `customerID` (Use PascalCase) to keep things consistent"
      ],
      "metadata": {
        "id": "tY0GP7Xamwef"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns={'tenure': 'Tenure', 'gender':'Gender', 'customerID': 'CustomerID'}, inplace=True)"
      ],
      "metadata": {
        "id": "aXYNA6o55Ayj"
      },
      "execution_count": 310,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['TotalCharges'].apply(lambda x: x.strip() == '').value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_j8wYJNXebWj",
        "outputId": "b9e276d7-96a3-4105-beaf-0022e7c92998"
      },
      "execution_count": 311,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    7032\n",
              "True       11\n",
              "Name: TotalCharges, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 311
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the category of examples affected by empty strings\n",
        "df['Churn'][df['TotalCharges'] == ' ']\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fOOHbWBHp8jB",
        "outputId": "d1187d71-e7e0-4e90-c5ff-8742ec847e47"
      },
      "execution_count": 312,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "488     No\n",
              "753     No\n",
              "936     No\n",
              "1082    No\n",
              "1340    No\n",
              "        ..\n",
              "3826    No\n",
              "4380    No\n",
              "5218    No\n",
              "6670    No\n",
              "6754    No\n",
              "Name: Churn, Length: 11, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 312
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Negative examples are sufficient (too many infact!),\n",
        "# we can just get rid of the rows with empty strings without replacement\n",
        "# Exclude rows with TotalCharges culomns contains white space\n",
        "df = df.loc[~df['TotalCharges'].str.contains(' ')]\n"
      ],
      "metadata": {
        "id": "3O_7EorQB_3s"
      },
      "execution_count": 313,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# transform TotalCharge col to float\n",
        "df.loc[:,'TotalCharges'] = df['TotalCharges'].astype(float)\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TaGOjbHKEk8j",
        "outputId": "f78d718e-0abc-4a7e-e2d9-e1bf67e7fd2e"
      },
      "execution_count": 314,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 7032 entries, 0 to 7042\n",
            "Data columns (total 21 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   CustomerID        7032 non-null   object \n",
            " 1   Gender            7032 non-null   object \n",
            " 2   SeniorCitizen     7032 non-null   int64  \n",
            " 3   Partner           7032 non-null   object \n",
            " 4   Dependents        7032 non-null   object \n",
            " 5   Tenure            7032 non-null   int64  \n",
            " 6   PhoneService      7032 non-null   object \n",
            " 7   MultipleLines     7032 non-null   object \n",
            " 8   InternetService   7032 non-null   object \n",
            " 9   OnlineSecurity    7032 non-null   object \n",
            " 10  OnlineBackup      7032 non-null   object \n",
            " 11  DeviceProtection  7032 non-null   object \n",
            " 12  TechSupport       7032 non-null   object \n",
            " 13  StreamingTV       7032 non-null   object \n",
            " 14  StreamingMovies   7032 non-null   object \n",
            " 15  Contract          7032 non-null   object \n",
            " 16  PaperlessBilling  7032 non-null   object \n",
            " 17  PaymentMethod     7032 non-null   object \n",
            " 18  MonthlyCharges    7032 non-null   float64\n",
            " 19  TotalCharges      7032 non-null   float64\n",
            " 20  Churn             7032 non-null   object \n",
            "dtypes: float64(2), int64(2), object(17)\n",
            "memory usage: 1.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for accuracy (posible values in categorical data) for each column manually\n",
        "df[\"PaperlessBilling\"].value_counts()\n",
        "# df[\"Churn\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KflUEEJt6-sz",
        "outputId": "1c8c80d1-4858-4370-8e07-4c5690107937"
      },
      "execution_count": 315,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Yes    4168\n",
              "No     2864\n",
              "Name: PaperlessBilling, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for class imbalance\n",
        "class_counts = df[\"Churn\"].value_counts()\n",
        "print(class_counts)\n",
        "print(class_counts / len(df) * 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1pjk6wRrIRE8",
        "outputId": "897ec183-8a16-462b-f00c-8cb7d406f332"
      },
      "execution_count": 316,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No     5163\n",
            "Yes    1869\n",
            "Name: Churn, dtype: int64\n",
            "No    73.4\n",
            "Yes   26.6\n",
            "Name: Churn, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---------------------------------------\n",
        "*Feature Engineering*\n",
        "---------------------------------------\n",
        "  - Feature encoding (convert feature values into a format the model can understand)\n",
        "  - Find feature(s) whose raw values correlate with the label (1.0 for perfect positive correlation, -1.0 for perfect negative correlation, 0 for no correlation)"
      ],
      "metadata": {
        "id": "Z9-i7TkZNqk_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop customerID, its not used\n",
        "df.drop(columns=['CustomerID'], inplace=True)\n",
        "\n",
        "# Define a mapping for each categorical column\n",
        "label_mappings = {}\n",
        "\n",
        "# Loop through each categorical column and perform label encoding\n",
        "for column in df.select_dtypes(include=['object']).columns:\n",
        "  unique_labels = df[column].unique()\n",
        "  label_mapping = {label: index for index, label in enumerate(unique_labels)}\n",
        "  df[column] = df[column].map(label_mapping)\n",
        "  label_mappings[column] = label_mapping\n",
        "print(df)"
      ],
      "metadata": {
        "id": "xqASDrUclRsH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2d226a8-c6b7-47ef-9a3e-01177929bb96"
      },
      "execution_count": 317,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Gender  SeniorCitizen  Partner  Dependents  Tenure  PhoneService  \\\n",
            "0          0              0        0           0       1             0   \n",
            "1          1              0        1           0      34             1   \n",
            "2          1              0        1           0       2             1   \n",
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            "7041       1              1        0           0       4             1   \n",
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            "\n",
            "      MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
            "0                 0                0               0             0   \n",
            "1                 1                0               1             1   \n",
            "2                 1                0               1             0   \n",
            "3                 0                0               1             1   \n",
            "4                 1                1               0             1   \n",
            "...             ...              ...             ...           ...   \n",
            "7038              2                0               1             1   \n",
            "7039              2                1               0             0   \n",
            "7040              0                0               1             1   \n",
            "7041              2                1               0             1   \n",
            "7042              1                1               1             1   \n",
            "\n",
            "      DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
            "0                    0            0            0                0         0   \n",
            "1                    1            0            0                0         1   \n",
            "2                    0            0            0                0         0   \n",
            "3                    1            1            0                0         1   \n",
            "4                    0            0            0                0         0   \n",
            "...                ...          ...          ...              ...       ...   \n",
            "7038                 1            1            1                1         1   \n",
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            "7041                 0            0            0                0         0   \n",
            "7042                 1            1            1                1         2   \n",
            "\n",
            "      PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
            "0                    0              0            29.9          29.9      0  \n",
            "1                    1              1            57.0        1889.5      0  \n",
            "2                    0              1            53.9         108.2      1  \n",
            "3                    1              2            42.3        1840.8      0  \n",
            "4                    0              0            70.7         151.7      1  \n",
            "...                ...            ...             ...           ...    ...  \n",
            "7038                 0              1            84.8        1990.5      0  \n",
            "7039                 0              3           103.2        7362.9      0  \n",
            "7040                 0              0            29.6         346.4      0  \n",
            "7041                 0              1            74.4         306.6      1  \n",
            "7042                 0              2           105.7        6844.5      0  \n",
            "\n",
            "[7032 rows x 20 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make it easier to train the model by maintaining a uniform scale across all features\n",
        "# Calculate the Z-scores of each column in the training set and\n",
        "# write those Z-scores into a new pandas DataFrame named train_df_norm.\n",
        "df_mean = df['Tenure'].mean()\n",
        "df_std = df['Tenure'].std()\n",
        "df['Tenure'] = (df['Tenure'] - df_mean)/df_std\n",
        "\n",
        "df_mean = df['MonthlyCharges'].mean()\n",
        "df_std = df['MonthlyCharges'].std()\n",
        "df['MonthlyCharges'] = (df['MonthlyCharges'] - df_mean)/df_std\n",
        "\n",
        "df_mean = df['TotalCharges'].mean()\n",
        "df_std = df['TotalCharges'].std()\n",
        "df['TotalCharges'] = (df['TotalCharges'] - df_mean)/df_std\n",
        "\n",
        "# Examine some of the values of the normalized training set. Notice that most\n",
        "# Z-scores fall between -2 and +2.\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "hWPliykWx02H",
        "outputId": "8857aec7-e63a-47a9-daae-1a709fe0b80b"
      },
      "execution_count": 318,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  SeniorCitizen  Partner  Dependents  Tenure  PhoneService  \\\n",
              "0       0              0        0           0    -1.3             0   \n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-76110df6-0315-418f-a6ec-8dfbb98fbca1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 318
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The last run did not have `TotalCharges`\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "cetfAKaKTa8F",
        "outputId": "7b9532e6-cfb5-45b7-b65e-ae1bb9492b26"
      },
      "execution_count": 319,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Gender  SeniorCitizen  Partner  Dependents  Tenure  PhoneService  \\\n",
              "count  7032.0         7032.0   7032.0      7032.0  7032.0        7032.0   \n",
              "mean      0.5            0.2      0.5         0.3    -0.0           0.9   \n",
              "std       0.5            0.4      0.5         0.5     1.0           0.3   \n",
              "min       0.0            0.0      0.0         0.0    -1.3           0.0   \n",
              "25%       0.0            0.0      0.0         0.0    -1.0           1.0   \n",
              "50%       1.0            0.0      1.0         0.0    -0.1           1.0   \n",
              "75%       1.0            0.0      1.0         1.0     0.9           1.0   \n",
              "max       1.0            1.0      1.0         1.0     1.6           1.0   \n",
              "\n",
              "       MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
              "count         7032.0           7032.0          7032.0        7032.0   \n",
              "mean             1.3              0.9             0.7           0.9   \n",
              "std              0.6              0.7             0.8           0.7   \n",
              "min              0.0              0.0             0.0           0.0   \n",
              "25%              1.0              0.0             0.0           0.0   \n",
              "50%              1.0              1.0             1.0           1.0   \n",
              "75%              2.0              1.0             1.0           1.0   \n",
              "max              2.0              2.0             2.0           2.0   \n",
              "\n",
              "       DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
              "count            7032.0       7032.0       7032.0           7032.0    7032.0   \n",
              "mean                0.8          0.7          0.8              0.8       0.7   \n",
              "std                 0.8          0.8          0.8              0.8       0.8   \n",
              "min                 0.0          0.0          0.0              0.0       0.0   \n",
              "25%                 0.0          0.0          0.0              0.0       0.0   \n",
              "50%                 1.0          1.0          1.0              1.0       0.0   \n",
              "75%                 1.0          1.0          1.0              1.0       1.0   \n",
              "max                 2.0          2.0          2.0              2.0       2.0   \n",
              "\n",
              "       PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
              "count            7032.0         7032.0          7032.0        7032.0 7032.0  \n",
              "mean                0.4            1.3             0.0          -0.0    0.3  \n",
              "std                 0.5            1.1             1.0           1.0    0.4  \n",
              "min                 0.0            0.0            -1.5          -1.0    0.0  \n",
              "25%                 0.0            0.0            -1.0          -0.8    0.0  \n",
              "50%                 0.0            1.0             0.2          -0.4    0.0  \n",
              "75%                 1.0            2.0             0.8           0.7    1.0  \n",
              "max                 1.0            3.0             1.8           2.8    1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f1e8331-3dce-4fb8-a2b0-93f0e048eaca\" class=\"colab-df-container\">\n",
              "    <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>count</th>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "      <td>7032.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.8</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2.8</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f1e8331-3dce-4fb8-a2b0-93f0e048eaca')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3f1e8331-3dce-4fb8-a2b0-93f0e048eaca button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3f1e8331-3dce-4fb8-a2b0-93f0e048eaca');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1a461f2c-f3a3-4e83-88e4-083080fe1c1d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1a461f2c-f3a3-4e83-88e4-083080fe1c1d')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1a461f2c-f3a3-4e83-88e4-083080fe1c1d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 319
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "RLHXPFvZTQoT",
        "outputId": "6a9a7858-ebd2-4530-ee44-67010169f0be"
      },
      "execution_count": 320,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Gender  SeniorCitizen  Partner  Dependents  Tenure  \\\n",
              "Gender               1.0           -0.0      0.0         0.0     0.0   \n",
              "SeniorCitizen       -0.0            1.0     -0.0        -0.2     0.0   \n",
              "Partner              0.0           -0.0      1.0        -0.5    -0.4   \n",
              "Dependents           0.0           -0.2     -0.5         1.0     0.2   \n",
              "Tenure               0.0            0.0     -0.4         0.2     1.0   \n",
              "...                  ...            ...      ...         ...     ...   \n",
              "PaperlessBilling     0.0           -0.2     -0.0         0.1    -0.0   \n",
              "PaymentMethod       -0.0           -0.1     -0.1         0.1     0.3   \n",
              "MonthlyCharges      -0.0            0.2     -0.1        -0.1     0.2   \n",
              "TotalCharges         0.0            0.1     -0.3         0.1     0.8   \n",
              "Churn               -0.0            0.2      0.1        -0.2    -0.4   \n",
              "\n",
              "                  PhoneService  MultipleLines  InternetService  \\\n",
              "Gender                    -0.0           -0.0             -0.0   \n",
              "SeniorCitizen              0.0            0.1             -0.0   \n",
              "Partner                   -0.0           -0.1             -0.0   \n",
              "Dependents                -0.0           -0.0              0.0   \n",
              "Tenure                     0.0            0.3             -0.0   \n",
              "...                        ...            ...              ...   \n",
              "PaperlessBilling          -0.0           -0.1              0.1   \n",
              "PaymentMethod             -0.0            0.0              0.0   \n",
              "MonthlyCharges             0.2            0.5             -0.3   \n",
              "TotalCharges               0.1            0.4             -0.2   \n",
              "Churn                      0.0            0.0             -0.0   \n",
              "\n",
              "                  OnlineSecurity  OnlineBackup  DeviceProtection  TechSupport  \\\n",
              "Gender                      -0.0           0.0               0.0          0.0   \n",
              "SeniorCitizen               -0.2          -0.1              -0.2         -0.2   \n",
              "Partner                     -0.1           0.1              -0.1         -0.1   \n",
              "Dependents                   0.2           0.1               0.2          0.2   \n",
              "Tenure                       0.1          -0.3               0.2          0.1   \n",
              "...                          ...           ...               ...          ...   \n",
              "PaperlessBilling             0.3           0.3               0.3          0.3   \n",
              "PaymentMethod                0.2           0.0               0.2          0.2   \n",
              "MonthlyCharges              -0.6          -0.7              -0.5         -0.6   \n",
              "TotalCharges                -0.2          -0.5              -0.1         -0.1   \n",
              "Churn                       -0.3          -0.1              -0.3         -0.3   \n",
              "\n",
              "                  StreamingTV  StreamingMovies  Contract  PaperlessBilling  \\\n",
              "Gender                    0.0             -0.0       0.0               0.0   \n",
              "SeniorCitizen            -0.1             -0.1      -0.1              -0.2   \n",
              "Partner                  -0.1             -0.1      -0.3              -0.0   \n",
              "Dependents                0.1              0.1       0.2               0.1   \n",
              "Tenure                    0.1              0.1       0.7              -0.0   \n",
              "...                       ...              ...       ...               ...   \n",
              "PaperlessBilling          0.2              0.2       0.2               1.0   \n",
              "PaymentMethod             0.1              0.1       0.4               0.1   \n",
              "MonthlyCharges           -0.4             -0.4      -0.1              -0.4   \n",
              "TotalCharges             -0.1             -0.1       0.5              -0.2   \n",
              "Churn                    -0.2             -0.2      -0.4              -0.2   \n",
              "\n",
              "                  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
              "Gender                     -0.0            -0.0           0.0   -0.0  \n",
              "SeniorCitizen              -0.1             0.2           0.1    0.2  \n",
              "Partner                    -0.1            -0.1          -0.3    0.1  \n",
              "Dependents                  0.1            -0.1           0.1   -0.2  \n",
              "Tenure                      0.3             0.2           0.8   -0.4  \n",
              "...                         ...             ...           ...    ...  \n",
              "PaperlessBilling            0.1            -0.4          -0.2   -0.2  \n",
              "PaymentMethod               1.0            -0.1           0.2   -0.3  \n",
              "MonthlyCharges             -0.1             1.0           0.7    0.2  \n",
              "TotalCharges                0.2             0.7           1.0   -0.2  \n",
              "Churn                      -0.3             0.2          -0.2    1.0  \n",
              "\n",
              "[20 rows x 20 columns]"
            ],
            "text/html": [
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              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
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              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents</th>\n",
              "      <td>0.0</td>\n",
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              "      <th>...</th>\n",
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              "      <td>-0.2</td>\n",
              "      <td>-0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PaymentMethod</th>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
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              "      <td>0.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>-0.7</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TotalCharges</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn</th>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20 rows × 20 columns</p>\n",
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              "            style=\"display:none;\">\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "            title=\"Suggest charts.\"\n",
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              "\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-32c30c41-729c-4f56-adaf-85aa8cd0cb67 button');\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 320
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "07pdeJz7A6aB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Feature Selection\n",
        "# @markdown ---------------------------------------------------------------------\n",
        "# @markdown  *Feature Selection*\n",
        "# @markdown  ---------------------------------------------------------------------\n",
        "# @markdown  *Columns with low correlation with the label (low predictive power).*\n",
        "# @markdown  * `Gender`\n",
        "# @markdown  * `PhoneService`\n",
        "# @markdown  * `MultipleLines`\n",
        "# @markdown  * `InternetService`\n",
        "# @markdown\n",
        "# @markdown  Exclude these.\n",
        "# @markdown Features used to train the model on e.g.,\n",
        "# @markdown ```Python\n",
        "# @markdown inputs = {\n",
        "# @markdown   'MonthlyCharges': tf.keras.Input(shape=(1,)),\n",
        "# @markdown }\n",
        "# @markdown ```\n",
        "\n",
        "\n",
        "columns_to_ignore = ['Churn', 'Gender', 'PhoneService', 'MultipleLines', 'InternetService']\n",
        "inputs = {}\n",
        "for column in df.columns:\n",
        "  if column not in columns_to_ignore:\n",
        "    inputs[column] = tf.keras.Input(shape=(1,))\n"
      ],
      "metadata": {
        "id": "wmKo0PmRBk5o"
      },
      "execution_count": 321,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----------------------------------------------------------------\n",
        "*Create & Train Model*\n",
        "----------------------------------------------------------------"
      ],
      "metadata": {
        "id": "NcvpONF0Yz2Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Define the functions that create and train a model.\n",
        "# @markdown The model used is a simple binary classification model. It takes multiple input features and concatenates them into a single tensor using a Concatenate layer. The concatenated tensor is then passed through a Dense layer with a sigmoid activation function, producing a binary output.\n",
        "# @markdown\n",
        "# @markdown   **Why this model:**\n",
        "# @markdown\n",
        "# @markdown   The model is suitable for binary classification tasks, which aligns with the problem of Telco Customer Churn prediction (churn or not churn).\n",
        "# @markdown   It uses the RMSprop optimizer, which adapts the learning rate during training and is a good choice for deep learning models.\n",
        "# @markdown   The binary cross-entropy loss function is appropriate for binary classification problems.\n",
        "# @markdown   It allows for the inclusion of custom evaluation metrics (specified by METRICS).\n",
        "def create_model(my_inputs, my_learning_rate, METRICS):\n",
        "  # Use a Concatenate layer to concatenate the input layers into a single tensor.\n",
        "  # as input for the Dense layer. Ex: [input_1[0][0], input_2[0][0]]\n",
        "  concatenated_inputs = tf.keras.layers.Concatenate()(my_inputs.values())\n",
        "  dense = layers.Dense(units=1, name='dense_layer', activation=tf.sigmoid)\n",
        "  dense_output = dense(concatenated_inputs)\n",
        "  \"\"\"Create and compile a simple classification model.\"\"\"\n",
        "  my_outputs = {\n",
        "    'dense': dense_output,\n",
        "  }\n",
        "  model = tf.keras.Model(inputs=my_inputs, outputs=my_outputs)\n",
        "\n",
        "  # Call the compile method to construct the layers into a model that\n",
        "  # TensorFlow can execute.\n",
        "  model.compile(optimizer=tf.keras.optimizers.experimental.RMSprop(learning_rate=my_learning_rate),\n",
        "                loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "                metrics=METRICS)\n",
        "  return model\n",
        "\n",
        "\n",
        "def train_model(model, dataset, epochs, label_name,\n",
        "                batch_size=None, shuffle=True):\n",
        "  \"\"\"Feed a dataset into the model in order to train it.\"\"\"\n",
        "\n",
        "  # The x parameter of tf.keras.Model.fit can be a list of arrays, where\n",
        "  # each array contains the data for one feature.  Here, we're passing\n",
        "  # every column in the dataset. The feature_layer will filter\n",
        "  # away most of those columns, leaving only the desired columns and their\n",
        "  # representations as features.\n",
        "  features = {name:np.array(value) for name, value in dataset.items()}\n",
        "  label = np.array(features.pop(label_name))\n",
        "  history = model.fit(x=features, y=label, batch_size=batch_size,\n",
        "                      epochs=epochs, shuffle=shuffle)\n",
        "\n",
        "  # The list of epochs is stored separately from the rest of history.\n",
        "  epochs = history.epoch\n",
        "\n",
        "  # Isolate the classification metric for each epoch.\n",
        "  hist = pd.DataFrame(history.history)\n",
        "\n",
        "  return epochs, hist\n",
        "\n",
        "print(\"Defined the create_model and train_model functions.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nrtPBHRpKMPn",
        "outputId": "ce2ac258-aafa-4384-85b3-045f5e4b3fe3"
      },
      "execution_count": 322,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Defined the create_model and train_model functions.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Define the plotting function.\n",
        "def plot_curve(epochs, hist, list_of_metrics):\n",
        "  \"\"\"Plot a curve of one or more classification metrics vs. epoch.\"\"\"\n",
        "  # list_of_metrics:\n",
        "  # https://www.tensorflow.org/tutorials/structured_data/imbalanced_data#define_the_model_and_metrics\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel(\"Epoch\")\n",
        "  plt.ylabel(\"Value\")\n",
        "\n",
        "  for m in list_of_metrics:\n",
        "    x = hist[m]\n",
        "    plt.plot(epochs[1:], x[1:], label=m)\n",
        "\n",
        "  plt.legend()\n",
        "\n",
        "print(\"Defined the plot_curve function.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fL07v7jiKo3m",
        "outputId": "d7a6888a-402b-4271-9750-2bb933b744de"
      },
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Defined the plot_curve function.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Split Data into Traning & Test Sets\n",
        "\n",
        "df = df.reindex(np.random.permutation(df.index)) # shuffle the training set\n",
        "\n",
        "X = df.drop('Churn', axis=1)  # Adjust 'target_column' to your target variable\n",
        "y = df['Churn']\n",
        "\n",
        "# Split the data into train and test sets using sklearn\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "y_train = pd.DataFrame(y_train, columns=['Churn'])\n",
        "y_test = pd.DataFrame(y_test, columns=['Churn'])\n",
        "\n",
        "# Concatenate X_train and y_train_df along columns (axis=1)\n",
        "train_dataset = pd.concat([X_train, y_train], axis=1)\n",
        "test_dataset = pd.concat([X_test, y_test], axis=1)\n"
      ],
      "metadata": {
        "id": "Wprci2qHalQd"
      },
      "execution_count": 324,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Invoke Model Creation, Plot Training Performance\n",
        "# The following variables are the hyperparameters.\n",
        "learning_rate = 0.02\n",
        "epochs = 40\n",
        "batch_size = 200\n",
        "classification_threshold = 0.52\n",
        "label_name = \"Churn\"\n",
        "\n",
        "METRICS = [\n",
        "      tf.keras.metrics.BinaryAccuracy(name='accuracy',\n",
        "                                      threshold=classification_threshold),\n",
        "      tf.keras.metrics.Precision(thresholds=classification_threshold,\n",
        "                                 name='precision'\n",
        "                                 ),\n",
        "      tf.keras.metrics.Recall(thresholds=classification_threshold,\n",
        "                              name=\"recall\"),\n",
        "      tf.keras.metrics.AUC(num_thresholds=100, name=\"auc\")\n",
        "]\n",
        "\n",
        "# Establish the model's topography.\n",
        "my_model = create_model(inputs, learning_rate, METRICS)\n",
        "\n",
        "# Train the model on the training set.\n",
        "epochs, hist = train_model(my_model, train_dataset, epochs,\n",
        "                           label_name, batch_size)\n",
        "\n",
        "# Plot metrics vs. epochs\n",
        "list_of_metrics_to_plot = ['accuracy', \"precision\", \"recall\", \"auc\"]\n",
        "plot_curve(epochs, hist, list_of_metrics_to_plot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iMVqSu2fVaBu",
        "outputId": "c03e219b-1b59-48b1-e0c0-2ae14fdd0cb8"
      },
      "execution_count": 335,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "29/29 [==============================] - 1s 2ms/step - loss: 0.4508 - accuracy: 0.7728 - precision: 0.6182 - recall: 0.3975 - auc: 0.8121\n",
            "Epoch 2/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4328 - accuracy: 0.7883 - precision: 0.6350 - recall: 0.4930 - auc: 0.8314\n",
            "Epoch 3/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4291 - accuracy: 0.7961 - precision: 0.6571 - recall: 0.4997 - auc: 0.8353\n",
            "Epoch 4/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4283 - accuracy: 0.7970 - precision: 0.6569 - recall: 0.5070 - auc: 0.8362\n",
            "Epoch 5/40\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.4278 - accuracy: 0.7956 - precision: 0.6514 - recall: 0.5096 - auc: 0.8369\n",
            "Epoch 6/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4266 - accuracy: 0.7980 - precision: 0.6587 - recall: 0.5109 - auc: 0.8382\n",
            "Epoch 7/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4264 - accuracy: 0.7982 - precision: 0.6634 - recall: 0.5010 - auc: 0.8380\n",
            "Epoch 8/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4272 - accuracy: 0.7993 - precision: 0.6572 - recall: 0.5242 - auc: 0.8373\n",
            "Epoch 9/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4268 - accuracy: 0.7972 - precision: 0.6525 - recall: 0.5196 - auc: 0.8378\n",
            "Epoch 10/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4262 - accuracy: 0.7998 - precision: 0.6602 - recall: 0.5209 - auc: 0.8385\n",
            "Epoch 11/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4260 - accuracy: 0.7996 - precision: 0.6678 - recall: 0.5017 - auc: 0.8387\n",
            "Epoch 12/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4264 - accuracy: 0.8023 - precision: 0.6681 - recall: 0.5209 - auc: 0.8384\n",
            "Epoch 13/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4252 - accuracy: 0.8004 - precision: 0.6644 - recall: 0.5149 - auc: 0.8390\n",
            "Epoch 14/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4246 - accuracy: 0.8036 - precision: 0.6782 - recall: 0.5076 - auc: 0.8402\n",
            "Epoch 15/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4258 - accuracy: 0.8046 - precision: 0.6732 - recall: 0.5262 - auc: 0.8393\n",
            "Epoch 16/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4260 - accuracy: 0.8012 - precision: 0.6644 - recall: 0.5216 - auc: 0.8390\n",
            "Epoch 17/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4252 - accuracy: 0.8025 - precision: 0.6623 - recall: 0.5362 - auc: 0.8394\n",
            "Epoch 18/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4258 - accuracy: 0.7995 - precision: 0.6646 - recall: 0.5076 - auc: 0.8388\n",
            "Epoch 19/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4250 - accuracy: 0.7995 - precision: 0.6715 - recall: 0.4924 - auc: 0.8398\n",
            "Epoch 20/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4262 - accuracy: 0.7991 - precision: 0.6593 - recall: 0.5176 - auc: 0.8386\n",
            "Epoch 21/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4248 - accuracy: 0.8005 - precision: 0.6636 - recall: 0.5182 - auc: 0.8400\n",
            "Epoch 22/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4249 - accuracy: 0.7991 - precision: 0.6602 - recall: 0.5156 - auc: 0.8396\n",
            "Epoch 23/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4262 - accuracy: 0.7975 - precision: 0.6667 - recall: 0.4884 - auc: 0.8386\n",
            "Epoch 24/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4253 - accuracy: 0.7988 - precision: 0.6685 - recall: 0.4937 - auc: 0.8396\n",
            "Epoch 25/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4255 - accuracy: 0.8025 - precision: 0.6698 - recall: 0.5182 - auc: 0.8391\n",
            "Epoch 26/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4249 - accuracy: 0.8012 - precision: 0.6675 - recall: 0.5143 - auc: 0.8398\n",
            "Epoch 27/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4255 - accuracy: 0.7995 - precision: 0.6613 - recall: 0.5156 - auc: 0.8395\n",
            "Epoch 28/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4251 - accuracy: 0.8005 - precision: 0.6652 - recall: 0.5143 - auc: 0.8398\n",
            "Epoch 29/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4247 - accuracy: 0.8000 - precision: 0.6627 - recall: 0.5163 - auc: 0.8396\n",
            "Epoch 30/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4259 - accuracy: 0.8011 - precision: 0.6708 - recall: 0.5056 - auc: 0.8388\n",
            "Epoch 31/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4248 - accuracy: 0.8004 - precision: 0.6672 - recall: 0.5083 - auc: 0.8398\n",
            "Epoch 32/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4253 - accuracy: 0.8002 - precision: 0.6603 - recall: 0.5236 - auc: 0.8397\n",
            "Epoch 33/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4257 - accuracy: 0.7980 - precision: 0.6670 - recall: 0.4917 - auc: 0.8389\n",
            "Epoch 34/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4251 - accuracy: 0.7991 - precision: 0.6691 - recall: 0.4950 - auc: 0.8397\n",
            "Epoch 35/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4253 - accuracy: 0.7996 - precision: 0.6658 - recall: 0.5063 - auc: 0.8397\n",
            "Epoch 36/40\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.4253 - accuracy: 0.8000 - precision: 0.6638 - recall: 0.5136 - auc: 0.8394\n",
            "Epoch 37/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4248 - accuracy: 0.8028 - precision: 0.6672 - recall: 0.5269 - auc: 0.8403\n",
            "Epoch 38/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4252 - accuracy: 0.8011 - precision: 0.6705 - recall: 0.5063 - auc: 0.8399\n",
            "Epoch 39/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4246 - accuracy: 0.7995 - precision: 0.6607 - recall: 0.5169 - auc: 0.8402\n",
            "Epoch 40/40\n",
            "29/29 [==============================] - 0s 2ms/step - loss: 0.4242 - accuracy: 0.8009 - precision: 0.6636 - recall: 0.5209 - auc: 0.8408\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---------------------------------------------------------------------\n",
        "*Evaluate Model Using Test Set*\n",
        "---------------------------------------------------------------------"
      ],
      "metadata": {
        "id": "9ux0Vgqtdn6t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = {name:np.array(value) for name, value in test_dataset.items()}\n",
        "label = np.array(features.pop(label_name))\n",
        "\n",
        "my_model.evaluate(x = features, y = label, batch_size=batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "685DSNB9dm5c",
        "outputId": "39d0a047-6304-4b66-d20e-25ca596488a6"
      },
      "execution_count": 336,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4125 - accuracy: 0.8067 - precision: 0.6406 - recall: 0.5663 - auc: 0.8463\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.41248080134391785,\n",
              " 0.8066808581352234,\n",
              " 0.640625,\n",
              " 0.5662983655929565,\n",
              " 0.8462581038475037]"
            ]
          },
          "metadata": {},
          "execution_count": 336
        }
      ]
    }
  ]
}