EDA.ipynb

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      "metadata": {
        "id": "Hc4HbZ-tFWE3"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px"
      ]
    },
    {
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        "id": "dYTIMznqFWE5",
        "outputId": "f6b49b36-cd73-41d0-96a9-81ea539420b1"
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          "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          29.85         29.85    No  \n",
              "1               Mailed check          56.95        1889.5    No  \n",
              "2               Mailed check          53.85        108.15   Yes  \n",
              "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
              "4           Electronic check          70.70        151.65   Yes  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
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              "      <th>OnlineSecurity</th>\n",
              "      <th>...</th>\n",
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              "      <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",
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              "      <th>0</th>\n",
              "      <td>7590-VHVEG</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5575-GNVDE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3668-QPYBK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7795-CFOCW</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9237-HQITU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5 rows × 21 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Read the dataset\n",
        "df = pd.read_csv(\"./data/churn.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eGc3xAzKFWE6",
        "outputId": "79f81e5c-326a-43b0-898f-bc09a93e8e77",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original dataset:\n",
            "\n",
            "Number of rows: 7043\n",
            "Number of columns(features): 21\n",
            "Columns: ['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn']\n",
            "\n",
            "Data types:\n",
            " 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\n",
            "\n",
            "Number of unique values in each column:\n",
            "customerID          7043\n",
            "gender                 2\n",
            "SeniorCitizen          2\n",
            "Partner                2\n",
            "Dependents             2\n",
            "tenure                73\n",
            "PhoneService           2\n",
            "MultipleLines          3\n",
            "InternetService        3\n",
            "OnlineSecurity         3\n",
            "OnlineBackup           3\n",
            "DeviceProtection       3\n",
            "TechSupport            3\n",
            "StreamingTV            3\n",
            "StreamingMovies        3\n",
            "Contract               3\n",
            "PaperlessBilling       2\n",
            "PaymentMethod          4\n",
            "MonthlyCharges      1585\n",
            "TotalCharges        6531\n",
            "Churn                  2\n",
            "dtype: int64\n",
            "\n",
            "Number of missing values in each column:\n",
            "No missing values\n"
          ]
        }
      ],
      "source": [
        "# Get a summary of the data\n",
        "def get_summary(df, text):\n",
        "    print(f\"{text}:\\n\")\n",
        "    print(\"Number of rows:\", df.shape[0])\n",
        "    print(\"Number of columns(features):\", df.shape[1])\n",
        "    print(\"Columns:\", df.columns.tolist())\n",
        "    print(\"\\nData types:\\n\", df.dtypes)\n",
        "    print(\"\\nNumber of unique values in each column:\")\n",
        "    print(df.nunique())\n",
        "    print(\"\\nNumber of missing values in each column:\")\n",
        "    missing_values = df.isnull().sum()\n",
        "    if missing_values[missing_values > 0].any():\n",
        "        print(missing_values[missing_values > 0])\n",
        "    else:\n",
        "        print(\"No missing values\")\n",
        "\n",
        "get_summary(df, \"Original dataset\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7256LXYtFWE7"
      },
      "source": [
        "##### There are 17 categorical features in this dataset:\n",
        "- **customerID:** Customer ID unique for each customer\n",
        "- **gender:** Whether the customer is a male or a female\n",
        "- **SeniorCitizen:** Whether the customer is a senior citizen or not (1, 0)\n",
        "- **Partner:** Whether the customer has a partner or not (Yes, No)\n",
        "- **Dependents:** Whether the customer has dependents or not (Yes, No)\n",
        "- **PhoneService:** Whether the customer has a phone service or not (Yes, No)\n",
        "- **MultipeLines:** Whether the customer has multiple lines or not (Yes, No, No phone service)\n",
        "- **InternetService:** Customer’s internet service provider (DSL, Fiber optic, No)\n",
        "- **OnlineSecurity:** Whether the customer has online security or not (Yes, No, No internet service)\n",
        "- **OnlineBackup:** Whether the customer has an online backup or not (Yes, No, No internet service)\n",
        "- **DeviceProtection:** Whether the customer has device protection or not (Yes, No, No internet service)\n",
        "- **TechSupport:** Whether the customer has tech support or not (Yes, No, No internet service)\n",
        "- **StreamingTV:** Whether the customer has streaming TV or not (Yes, No, No internet service)\n",
        "- **StreamingMovies:** Whether the customer has streaming movies or not (Yes, No, No internet service)\n",
        "- **Contract:** The contract term of the customer (Month-to-month, One year, Two years)\n",
        "- **PaperlessBilling:** The contract term of the customer (Month-to-month, One year, Two years)\n",
        "- **PaymentMethod:** The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))\n",
        "\n",
        "##### And 3 numerical features:\n",
        "- **Tenure:** Number of months the customer has stayed with the company\n",
        "- **MonthlyCharges:** The amount charged to the customer monthly\n",
        "- **TotalCharges:** The total amount charged to the customer\n",
        "\n",
        "##### The dataset includes information about:\n",
        "- **Customers who left within the last month**:\n",
        "  - Churn\n",
        "- **Services that each customer has signed up for**:\n",
        "  - PhoneService , MultipleLines , InternetService , OnlineSecurity , OnlineBackup , DeviceProtection , TechSupport , StreamingTV , StreamingMovies\n",
        "- **Customer account information**:\n",
        "  - tenure , Contract , PaperlessBilling , PaymentMethod , MonthlyCharges , TotalCharges\n",
        "- **Demographic info about customers**:\n",
        "  - gender , SeniorCitizen , Partner , Dependents\n",
        "\n",
        "##### Target variable:\n",
        "- **Churn**: Whether the customer churned or not (Yes or No)\n",
        "\n",
        "#### Explore target variable"
      ]
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          "metadata": {}
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      ],
      "source": [
        "target_col = df[\"Churn\"].value_counts().to_frame()\n",
        "target_col = target_col.reset_index()\n",
        "target_col = target_col.rename(columns={\"index\": \"Category\"})\n",
        "\n",
        "fig = px.pie(target_col, values=\"Churn\", names=\"Category\", title=\"Distribution of Churn\")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wENA81_7FWE_"
      },
      "source": [
        "We are trying to predict users that left the company in the previous month. \n",
        "It is a binary classification problem with an imbalanced target variable.\n",
        "- Churn: No - 73.5% (5174 of the customers did not churn)\n",
        "- Churn: Yes - 26.5% (1869 of the customers did churn)\n",
        "\n",
        "#### Explore Categorical features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R5HQ06qEFWE_"
      },
      "outputs": [],
      "source": [
        "# Define a bar chart function to plot the distribution of the target variable compared to the other features.\n",
        "def plot_bar_chart(feature, df=df):\n",
        "    # Groupby the categorical features\n",
        "    temp_df = df.groupby([feature, \"Churn\"]).size().reset_index()\n",
        "    temp_df = temp_df.rename(columns={0: \"Count\"})\n",
        "\n",
        "    # Calculate the value counts of each feature and its corresponding percentages\n",
        "    value_counts_df = df[feature].value_counts().to_frame().reset_index()\n",
        "    categories = [cat[1][0] for cat in value_counts_df.iterrows()]\n",
        "    num__list = [num[1][1] for num in value_counts_df.iterrows()]\n",
        "    div_list = [element / sum(num__list) for element in num__list]\n",
        "    percentage = [round(element * 100, 2) for element in div_list]\n",
        "\n",
        "    # Define string formatting for graph annotations\n",
        "    # Numeric values\n",
        "    def num_format(list_instance):\n",
        "        formatted_str = \"\"\n",
        "        for index, num in enumerate(list_instance):\n",
        "            if index < len(list_instance) - 2:\n",
        "                formatted_str = formatted_str + f\"{num}%, \"\n",
        "            elif index == len(list_instance) - 2:\n",
        "                formatted_str = formatted_str + f\"{num}% & \"\n",
        "            else:\n",
        "                formatted_str = formatted_str + f\"{num}%\"\n",
        "        return formatted_str\n",
        "    \n",
        "    #Categorical values\n",
        "    def str_format(list_instance):\n",
        "        formatted_str = \"\"\n",
        "        for index, cat in enumerate(list_instance):\n",
        "            if index < len(list_instance) - 2:\n",
        "                formatted_str = formatted_str + f\"{cat}, \"\n",
        "            elif index == len(list_instance) - 2:\n",
        "                formatted_str = formatted_str + f\"{cat} & \"\n",
        "            else:\n",
        "                formatted_str = formatted_str + f\"{cat}\"\n",
        "        return formatted_str\n",
        "\n",
        "    # Running the formatting functions\n",
        "    num_str = num_format(percentage)\n",
        "    cat_str = str_format(categories)\n",
        "\n",
        "    # Setting up the bar chart\n",
        "    fig = px.bar(temp_df, x=feature, y=\"Count\", color=\"Churn\", title=f\"Churn rate by {feature}\", \n",
        "        barmode=\"group\", color_discrete_sequence=[\"green\", \"red\"])\n",
        "    fig.add_annotation(\n",
        "        text=f'Value count of distribution of {cat_str} are<br>{num_str} percentage respectively.',\n",
        "        align='left',\n",
        "        showarrow=False,\n",
        "        xref='paper',\n",
        "        yref='paper',\n",
        "        x=1.4,\n",
        "        y=1.3,\n",
        "        bordercolor='black',\n",
        "        borderwidth=1)\n",
        "    fig.update_layout(margin=dict(r=400),) # margin space for the annotations on the right\n",
        "    return fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IveqmEWBFWFA"
      },
      "source": [
        "##### Visualize the features related to customers demographic "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U1o_sKW1FWFB",
        "outputId": "5fa0e26b-352e-4bf0-e5b8-ac61e77231ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the bar chart for the gender variable\n",
        "plot_bar_chart(\"gender\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MfBe_G82FWFC",
        "outputId": "b6af1cb5-05d3-467d-f8bd-bded1398cf42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "outputs": [
        {
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          "metadata": {}
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      ],
      "source": [
        "# Plot the bar chart for the SeniorCitizen variable\n",
        "df.loc[df[\"SeniorCitizen\"] == 0, \"SeniorCitizen\"] = \"No\" # Convert 0 to No in all instances\n",
        "df.loc[df[\"SeniorCitizen\"] == 1, \"SeniorCitizen\"] = \"Yes\" # Convert 1 to Yes in all instances\n",
        "\n",
        "plot_bar_chart(\"SeniorCitizen\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dDP9JaLiFWFD",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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      },
      "outputs": [
        {
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rate by Partner\"},\"barmode\":\"group\",\"annotations\":[{\"align\":\"left\",\"bordercolor\":\"black\",\"borderwidth\":1,\"showarrow\":false,\"text\":\"Value count of distribution of No & Yes are<br>51.7% & 48.3% percentage respectively.\",\"x\":1.4,\"xref\":\"paper\",\"y\":1.3,\"yref\":\"paper\"}],\"margin\":{\"r\":400}},                        {\"responsive\": true}                    ).then(function(){\n",
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          "metadata": {}
        }
      ],
      "source": [
        "# Plot the bar chart for the Partner variable\n",
        "plot_bar_chart(\"Partner\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FholXyBBFWFD",
        "outputId": "54bba5d5-9f53-4054-b8fd-a444c47495a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the bar chart for the Dependents variable\n",
        "plot_bar_chart(\"Dependents\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L5RT7ZzCFWFE"
      },
      "source": [
        "#### Demographic analysis Insight: \n",
        "- Gender and partner are almost equally distributed with approximate percentage values. \n",
        "- 71.4% of SeniorCitizen's churned within the last month. Company should investigate this further and try to understand why the churn rate is so high among senior citizens.\n",
        "- Customers without a partner, no dependencies have a high churn rate compared to customers with partner and dependents.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yIbs1Lz7FWFE"
      },
      "source": [
        "##### Visualize the features related to Services that each customer has signed up for"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DvusFPkpFWFE",
        "outputId": "eb2922a5-9cf3-4021-8776-d06939e14118",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_bar_chart(\"PhoneService\")\n",
        "plot_bar_chart(\"MultipleLines\")\n",
        "plot_bar_chart(\"InternetService\")\n",
        "plot_bar_chart(\"OnlineSecurity\")\n",
        "plot_bar_chart(\"OnlineBackup\")\n",
        "plot_bar_chart(\"DeviceProtection\")\n",
        "plot_bar_chart(\"TechSupport\")\n",
        "plot_bar_chart(\"StreamingTV\")\n",
        "plot_bar_chart(\"StreamingMovies\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TOUxF2cBFWFF"
      },
      "source": [
        "#### Services that each customer has signed up for Insight: \n",
        "These category of features shows significant variations across their values. \n",
        "- If a customer does not have a phone service, he/she cannot have multiple lines. \n",
        "- About 90.3% of the customers have phone services and have the higher rate to churn.\n",
        "- Customers who have Fibre optic as internet service are more likely to churn, this can happen due to high prices, competition, customer service, and many other reasons. \n",
        "  - Fiber optic service is much more expensive than DSL which may be one of the reasons why customers churn. \n",
        "- Customers with OnlineSecurity ,OnlineBackup ,DeviceProtection and TechSupport are more unlikely to churn. \n",
        "- Streaming service is not predictive for churn as it evenly distributed to yes and no options."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fuRQKPyHFWFF"
      },
      "source": [
        "##### Visualize the features related to Payment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EUT4PGT4FWFG",
        "outputId": "7b2462c8-3148-49f0-eedc-d307952d0b6a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
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rate by Contract\"},\"barmode\":\"group\",\"annotations\":[{\"align\":\"left\",\"bordercolor\":\"black\",\"borderwidth\":1,\"showarrow\":false,\"text\":\"Value count of distribution of Month-to-month, Two year & One year are<br>55.02%, 24.07% & 20.91% percentage respectively.\",\"x\":1.4,\"xref\":\"paper\",\"y\":1.3,\"yref\":\"paper\"}],\"margin\":{\"r\":400}},                        {\"responsive\": true}                    ).then(function(){\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_bar_chart(\"Contract\")\n",
        "plot_bar_chart(\"PaperlessBilling\")\n",
        "plot_bar_chart(\"PaymentMethod\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfGPOwUZFWFG"
      },
      "source": [
        "#### Payment insights\n",
        "- The shorter the contract the higher churn rate because those with longer plans face additional hurdles when canceling early.\n",
        "  - This clearly explains the motivation for companies to have long-term relationship with their customers. \n",
        "- Churn Rate is higher for the customers who prefer paperless billing, About 59.2% of the customers make paperless billing. \n",
        "- Customers who pay with electronic check are more likely to churn and this kind of payment is more common than other payment types."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASKLbHnDFWFG"
      },
      "source": [
        "#### Explore Numeric features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_6CcL8s5FWFH",
        "outputId": "799d1b06-eaec-4e92-918b-0ef5dbc286c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['tenure', 'MonthlyCharges']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# find numerical variables\n",
        "numerical_variables = [col for col in df.columns if df[col].dtype != \"object\"]\n",
        "numerical_variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EWtn_mW5FWFH",
        "outputId": "48c9dd20-79cd-420a-8bc0-1900908e85cf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "could not convert string to float: \n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    df[\"TotalCharges\"] = df[\"TotalCharges\"].astype(float)\n",
        "except ValueError as e:\n",
        "    print(e)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lEytJB-CFWFH"
      },
      "outputs": [],
      "source": [
        "# There are missing values in TotalCharges column in form of blankspaces.\n",
        "df[\"TotalCharges\"] = pd.to_numeric(df[\"TotalCharges\"], errors=\"coerce\")\n",
        "# If errors = coerce --> then invalid parsing will be set as NaN.\n",
        "# Fill the missing values with the median of the column\n",
        "df[\"TotalCharges\"] = df[\"TotalCharges\"].fillna(df[\"TotalCharges\"].median())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k_sY8ESeFWFI"
      },
      "outputs": [],
      "source": [
        "# Define the histogram plotting function for the numerical variables\n",
        "def plot_histogram(feature):\n",
        "    group_df = df.groupby([feature, \"Churn\"]).size().reset_index()\n",
        "    group_df = group_df.rename(columns={0: \"Count\"})\n",
        "    fig = px.histogram(group_df, x=feature, y=\"Count\", color=\"Churn\", marginal=\"box\", \n",
        "        title = f\"Churn rate frequency to {feature} distribution\", color_discrete_sequence=[\"green\", \"red\"])\n",
        "    fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qAe55Z07FWFI",
        "outputId": "dc41713c-4547-40c2-8b12-047d036ae76a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
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          "metadata": {}
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      ],
      "source": [
        "plot_histogram(\"tenure\")\n",
        "plot_histogram(\"MonthlyCharges\")\n",
        "plot_histogram(\"TotalCharges\")"
      ]
    },
    {
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      "metadata": {
        "id": "o_00EOFxFWFI"
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      "source": [
        "#### Insights on Customer account information\n",
        "- The tenure histogram is rightly skewed and shows that majority of customers has been with the telecom company for just the first few months (0-9 months) and the highest rate of churn is also in that first few months (0-9months). \n",
        "  - 75% of customers who end up leaving Telcom company do so within their first 30 months. \n",
        "- The monthly charge histogram shows that clients with higher monthly charges have a higher churn rate (This suggests that discounts and promotions can be an enticing reason for customers to stay). \n",
        "- The total charge trend is quite depict due to variation in frequency. \n",
        "\n",
        "Lets bin the numeric features into 3 sections based on quantiles (low, medium and high to get more information from it)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 1000
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      "source": [
        "# Create an empty dataframe\n",
        "bin_df = pd.DataFrame()\n",
        "\n",
        "# Update the dataframe with the bins\n",
        "bin_df[\"tenure_bins\"] = pd.qcut(df[\"tenure\"], q=3, labels=[\"low\", \"medium\", \"high\"])\n",
        "bin_df[\"MonthlyCharges_bins\"] = pd.qcut(df[\"MonthlyCharges\"], q=3, labels=[\"low\", \"medium\", \"high\"])\n",
        "bin_df[\"TotalCharges_bins\"] = pd.qcut(df[\"TotalCharges\"], q=3, labels=[\"low\", \"medium\", \"high\"])\n",
        "bin_df[\"Churn\"] = df[\"Churn\"]\n",
        "\n",
        "# Plot the bar chart of the binned variables\n",
        "plot_bar_chart(\"tenure_bins\", bin_df)\n",
        "plot_bar_chart(\"MonthlyCharges_bins\", bin_df)\n",
        "plot_bar_chart(\"TotalCharges_bins\", bin_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2sDylXoFWFJ"
      },
      "source": [
        "- Based on binning, the low tenure and high monthly charge bins have higher churn rates as supported with the previous analysis. \n",
        "- While the low Total charge bin has a higher churn rate."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-mkxmF9FWFJ"
      },
      "source": [
        "#### Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RcacRO-fFWFJ"
      },
      "outputs": [],
      "source": [
        "# The customerID column is used for identification purposes, it will not bring any information to the model.\n",
        "df.drop([\"customerID\"], axis=1, inplace=True)\n",
        "\n",
        "# Encode categorical variables\n",
        "# Define the function to encode categorical variables\n",
        "def binary_map(feature):\n",
        "    return feature.map({\"Yes\": 1, \"No\": 0})\n",
        "\n",
        "# Encode the target variable\n",
        "df[\"Churn\"] = df[[\"Churn\"]].apply(binary_map)\n",
        "\n",
        "# Encode gender variable\n",
        "df[\"gender\"] = df[\"gender\"].map({\"Male\":1, \"Female\": 0})\n",
        "\n",
        "# Encode the other binary variables\n",
        "binary_list = [\"SeniorCitizen\", \"Partner\", \"Dependents\", \"PhoneService\", \"PaperlessBilling\"]\n",
        "df[binary_list] = df[binary_list].apply(binary_map)\n",
        "\n",
        "# Encode the other categorical variables with more than 2 categories\n",
        "df = pd.get_dummies(df, drop_first=True)"
      ]
    },
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      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 837
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      "outputs": [
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      "source": [
        "# Check the correlation between the variables\n",
        "corr = df.corr()\n",
        "\n",
        "fig = px.imshow(corr, width=800, height=800)\n",
        "fig.show()"
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        "id": "ZqzgeXx8FWFK"
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      "source": [
        "Correlation is a statistical term is a measure on linear relationship with two variables. Features with high correlation are more linearly dependent and have almost the same effect on the dependent variable. So when two features have a high correlation(multicollinearity), we can drop one of the two features."
      ]
    },
    {
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning:\n",
            "\n",
            "pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "\n"
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        {
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          "name": "stdout",
          "text": [
            "                 Generalized Linear Model Regression Results                  \n",
            "==============================================================================\n",
            "Dep. Variable:                  Churn   No. Observations:                 7043\n",
            "Model:                            GLM   Df Residuals:                     7019\n",
            "Model Family:                Binomial   Df Model:                           23\n",
            "Link Function:                  logit   Scale:                          1.0000\n",
            "Method:                          IRLS   Log-Likelihood:                -2914.7\n",
            "Date:                Mon, 21 Mar 2022   Deviance:                       5829.3\n",
            "Time:                        10:51:06   Pearson chi2:                 8.04e+03\n",
            "No. Iterations:                     7                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "=========================================================================================================\n",
            "                                            coef    std err          z      P>|z|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------------------------------\n",
            "Intercept                                 0.8274      0.748      1.106      0.269      -0.639       2.294\n",
            "gender                                   -0.0219      0.065     -0.338      0.736      -0.149       0.105\n",
            "SeniorCitizen                             0.2151      0.085      2.545      0.011       0.049       0.381\n",
            "Partner                                  -0.0027      0.078     -0.035      0.972      -0.155       0.150\n",
            "Dependents                               -0.1538      0.090     -1.714      0.087      -0.330       0.022\n",
            "tenure                                   -0.0594      0.006     -9.649      0.000      -0.071      -0.047\n",
            "PhoneService                              0.5036      0.692      0.728      0.467      -0.852       1.860\n",
            "PaperlessBilling                          0.3418      0.074      4.590      0.000       0.196       0.488\n",
            "MonthlyCharges                           -0.0404      0.032     -1.272      0.203      -0.103       0.022\n",
            "TotalCharges                              0.0003   7.01e-05      4.543      0.000       0.000       0.000\n",
            "MultipleLines_No_phone_service            0.3238      0.106      3.061      0.002       0.116       0.531\n",
            "MultipleLines_Yes                         0.4469      0.177      2.524      0.012       0.100       0.794\n",
            "InternetService_Fiber_optic               1.7530      0.798      2.198      0.028       0.190       3.316\n",
            "InternetService_No                       -0.2559      0.115     -2.220      0.026      -0.482      -0.030\n",
            "OnlineSecurity_No_internet_service       -0.2559      0.115     -2.220      0.026      -0.482      -0.030\n",
            "OnlineSecurity_Yes                       -0.2055      0.179     -1.150      0.250      -0.556       0.145\n",
            "OnlineBackup_No_internet_service         -0.2559      0.115     -2.220      0.026      -0.482      -0.030\n",
            "OnlineBackup_Yes                          0.0258      0.175      0.147      0.883      -0.318       0.369\n",
            "DeviceProtection_No_internet_service     -0.2559      0.115     -2.220      0.026      -0.482      -0.030\n",
            "DeviceProtection_Yes                      0.1477      0.176      0.838      0.402      -0.198       0.493\n",
            "TechSupport_No_internet_service          -0.2559      0.115     -2.220      0.026      -0.482      -0.030\n",
            "TechSupport_Yes                          -0.1789      0.180     -0.991      0.322      -0.533       0.175\n",
            "StreamingTV_No_internet_service          -0.2559      0.115     -2.220      0.026      -0.482      -0.030\n",
            "StreamingTV_Yes                           0.5912      0.326      1.813      0.070      -0.048       1.230\n",
            "StreamingMovies_No_internet_service      -0.2559      0.115     -2.220      0.026      -0.482      -0.030\n",
            "StreamingMovies_Yes                       0.6038      0.326      1.850      0.064      -0.036       1.244\n",
            "Contract_One_year                        -0.6671      0.107     -6.208      0.000      -0.878      -0.456\n",
            "Contract_Two_year                        -1.3896      0.176     -7.904      0.000      -1.734      -1.045\n",
            "PaymentMethod_Credit_card__automatic_    -0.0865      0.114     -0.758      0.448      -0.310       0.137\n",
            "PaymentMethod_Electronic_check            0.3057      0.094      3.236      0.001       0.121       0.491\n",
            "PaymentMethod_Mailed_check               -0.0567      0.115     -0.493      0.622      -0.282       0.168\n",
            "=========================================================================================================\n"
          ]
        }
      ],
      "source": [
        "import statsmodels.api as sm\n",
        "import statsmodels.formula.api as smf\n",
        "\n",
        "#Change variable name seperators to '_'\n",
        "all_columns = [column.replace(\" \", \"_\").replace(\"(\", \"_\").replace(\")\", \"_\").replace(\"-\", \"_\") for column in df.columns]\n",
        "\n",
        "# Rename the columns\n",
        "df.columns = all_columns\n",
        "\n",
        "# Prepare the data for the GLM formula\n",
        "glm_columns = [column for column in all_columns if column not in [\"Churn\"]]\n",
        "glm_columns = \"+\".join(map(str, glm_columns))\n",
        "\n",
        "# Fitting it to the Generalized Linear Model\n",
        "glm_model = smf.glm(formula=f\"Churn ~ {glm_columns}\", data=df, family=sm.families.Binomial())\n",
        "glm_results = glm_model.fit()\n",
        "print(glm_results.summary())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yDJGyqFWFWFL",
        "outputId": "698d7f17-bd9d-43fb-94a2-7d5810292924",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept                                2.287343\n",
              "gender                                   0.978355\n",
              "SeniorCitizen                            1.239957\n",
              "Partner                                  0.997312\n",
              "Dependents                               0.857471\n",
              "tenure                                   0.942322\n",
              "PhoneService                             1.654668\n",
              "PaperlessBilling                         1.407543\n",
              "MonthlyCharges                           0.960432\n",
              "TotalCharges                             1.000318\n",
              "MultipleLines_No_phone_service           1.382358\n",
              "MultipleLines_Yes                        1.563475\n",
              "InternetService_Fiber_optic              5.771657\n",
              "InternetService_No                       0.774257\n",
              "OnlineSecurity_No_internet_service       0.774257\n",
              "OnlineSecurity_Yes                       0.814269\n",
              "OnlineBackup_No_internet_service         0.774257\n",
              "OnlineBackup_Yes                         1.026127\n",
              "DeviceProtection_No_internet_service     0.774257\n",
              "DeviceProtection_Yes                     1.159152\n",
              "TechSupport_No_internet_service          0.774257\n",
              "TechSupport_Yes                          0.836193\n",
              "StreamingTV_No_internet_service          0.774257\n",
              "StreamingTV_Yes                          1.806134\n",
              "StreamingMovies_No_internet_service      0.774257\n",
              "StreamingMovies_Yes                      1.829067\n",
              "Contract_One_year                        0.513185\n",
              "Contract_Two_year                        0.249179\n",
              "PaymentMethod_Credit_card__automatic_    0.917142\n",
              "PaymentMethod_Electronic_check           1.357617\n",
              "PaymentMethod_Mailed_check               0.944913\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "np.exp(glm_results.params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "urRm3NW0FWFL"
      },
      "source": [
        "#### Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cKp1GKmTFWFL"
      },
      "outputs": [],
      "source": [
        "# Scale numerical variables to the range of 0 to 1(default range) using MinMaxScaler.\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range=(0, 1))\n",
        "df[\"tenure\"] = sc.fit_transform(df[[\"tenure\"]])\n",
        "df[\"MonthlyCharges\"] = sc.fit_transform(df[[\"MonthlyCharges\"]])\n",
        "df[\"TotalCharges\"] = sc.fit_transform(df[[\"TotalCharges\"]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5EPZyknzFWFL"
      },
      "source": [
        "#### Create a baseline model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L1trIw-oFWFL"
      },
      "outputs": [],
      "source": [
        "# Import various classification algorithms\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# Import performance evaluation metrics\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data into train and test sets\n",
        "X = df.drop([\"Churn\"], axis=1)\n",
        "y = df[\"Churn\"]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tRUlzWygFWFM"
      },
      "outputs": [],
      "source": [
        "def modeling(alg, alg_name, params={}):\n",
        "    model = alg(**params) # **params is used to pass the parameters as a dictionary to the model.\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    \n",
        "    #Performance evaluation\n",
        "    def print_scores(alg, y_true, y_pred):\n",
        "        print(alg_name)\n",
        "        acc_score = accuracy_score(y_true, y_pred)\n",
        "        print(f\"Accuracy: {acc_score}\")\n",
        "        pre_score = precision_score(y_true, y_pred)\n",
        "        print(f\"Precision: {pre_score}\")\n",
        "        rec_score = recall_score(y_true, y_pred)                            \n",
        "        print(f\"Recall: {rec_score}\")\n",
        "        f_score = f1_score(y_true, y_pred, average=\"weighted\")\n",
        "        print(f\"F1 Score: {f_score}\")\n",
        "\n",
        "    print_scores(alg, y_test, y_pred)\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LqkrX7l1FWFM",
        "outputId": "ff77b02a-ec8c-4b60-8ad6-7d3ca6de5479",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression\n",
            "Accuracy: 0.812588736393753\n",
            "Precision: 0.6877637130801688\n",
            "Recall: 0.5679442508710801\n",
            "F1 Score: 0.8065959317426014\n"
          ]
        }
      ],
      "source": [
        "# Logistic Regression\n",
        "log_model = modeling(LogisticRegression, \"Logistic Regression\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZT-2Bx6kFWFM"
      },
      "source": [
        "#### Feature selection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CVRXHvrNFWFM",
        "outputId": "f5317a1d-8506-43aa-f5cc-e174705d7da4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RFECV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "      estimator=LogisticRegression(), scoring='accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Feature selection to improve the model\n",
        "from sklearn.feature_selection import RFECV\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "log = LogisticRegression()\n",
        "rfecv = RFECV(estimator=log, cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True), scoring=\"accuracy\")\n",
        "rfecv.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8wHRP02sFWFN",
        "outputId": "9f92d7da-cf35-4659-cb30-85856fde5238",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:103: FutureWarning:\n",
            "\n",
            "The `grid_scores_` attribute is deprecated in version 1.0 in favor of `cv_results_` and will be removed in version 1.2.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal number of features: 22\n"
          ]
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
        "plt.grid()\n",
        "plt.xticks(range(1, X.shape[1] + 1))\n",
        "plt.xlabel(\"Number of features selected\")\n",
        "plt.ylabel(\"Cross validation score\")\n",
        "plt.title(\"Recursive Feature Elimination with Cross Validation\")\n",
        "plt.show()\n",
        "\n",
        "print(f\"Optimal number of features: {rfecv.n_features_}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6OBHIMY-FWFN",
        "outputId": "9cd47e1d-2f5c-4b72-b166-f44e07320bdb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X dimensions: (7043, 30)\n",
            "X columns: ['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'PaperlessBilling', 'MonthlyCharges', 'TotalCharges', 'MultipleLines_No_phone_service', 'MultipleLines_Yes', 'InternetService_Fiber_optic', 'InternetService_No', 'OnlineSecurity_No_internet_service', 'OnlineSecurity_Yes', 'OnlineBackup_No_internet_service', 'OnlineBackup_Yes', 'DeviceProtection_No_internet_service', 'DeviceProtection_Yes', 'TechSupport_No_internet_service', 'TechSupport_Yes', 'StreamingTV_No_internet_service', 'StreamingTV_Yes', 'StreamingMovies_No_internet_service', 'StreamingMovies_Yes', 'Contract_One_year', 'Contract_Two_year', 'PaymentMethod_Credit_card__automatic_', 'PaymentMethod_Electronic_check', 'PaymentMethod_Mailed_check']\n",
            "X_rfe dimensions: (7043, 22)\n",
            "X_rfe columns: ['SeniorCitizen', 'Dependents', 'tenure', 'PhoneService', 'PaperlessBilling', 'MonthlyCharges', 'TotalCharges', 'MultipleLines_No_phone_service', 'MultipleLines_Yes', 'InternetService_Fiber_optic', 'InternetService_No', 'OnlineSecurity_Yes', 'OnlineBackup_No_internet_service', 'TechSupport_No_internet_service', 'TechSupport_Yes', 'StreamingTV_No_internet_service', 'StreamingTV_Yes', 'StreamingMovies_No_internet_service', 'StreamingMovies_Yes', 'Contract_One_year', 'Contract_Two_year', 'PaymentMethod_Electronic_check']\n"
          ]
        }
      ],
      "source": [
        "# Saving dataframe with optimal features\n",
        "X_rfe = X.iloc[:, rfecv.support_]\n",
        "\n",
        "# Overview of the optimal features in comparison to the original dataframe\n",
        "print(f\"X dimensions: {X.shape}\")\n",
        "print(f\"X columns: {X.columns.tolist()}\")\n",
        "print(f\"X_rfe dimensions: {X_rfe.shape}\")\n",
        "print(f\"X_rfe columns: {X_rfe.columns.tolist()}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MWGIEk5RFWFN"
      },
      "outputs": [],
      "source": [
        "# Split the data with the optimal features into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_rfe, y, test_size=0.3, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3q-VqjBpFWFN",
        "outputId": "144aad07-7b60-4a15-e242-86fca49ee885",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression\n",
            "Accuracy: 0.8116422148603881\n",
            "Precision: 0.6856540084388185\n",
            "Recall: 0.5662020905923345\n",
            "F1 Score: 0.805619143519079\n"
          ]
        }
      ],
      "source": [
        "# Running logistic regression\n",
        "log_model = modeling(LogisticRegression, \"Logistic Regression\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cO81P9p1FWFO",
        "outputId": "48e60419-4bc9-4236-90b7-b6366e811223",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC\n",
            "Accuracy: 0.8035967818267865\n",
            "Precision: 0.6943765281173594\n",
            "Recall: 0.49477351916376305\n",
            "F1 Score: 0.7921096786778191\n"
          ]
        }
      ],
      "source": [
        "# Running SVC\n",
        "svc_model = modeling(SVC, \"SVC\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ws_lcysbFWFO",
        "outputId": "d1345bd4-c674-4f03-93e6-8d0c23037129",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Classifier\n",
            "Accuracy: 0.7865593942262187\n",
            "Precision: 0.6460807600950119\n",
            "Recall: 0.4738675958188153\n",
            "F1 Score: 0.7752027442539716\n"
          ]
        }
      ],
      "source": [
        "# Running Random Forest Classifier\n",
        "rf_model = modeling(RandomForestClassifier, \"Random Forest Classifier\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WqcUfQEgFWFO",
        "outputId": "cb8f9e04-be5d-4985-bba0-70e220e6b15b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Classifier\n",
            "Accuracy: 0.7335541883577852\n",
            "Precision: 0.5105162523900574\n",
            "Recall: 0.4651567944250871\n",
            "F1 Score: 0.7295320167954189\n"
          ]
        }
      ],
      "source": [
        "# Running Decision Tree Classifier\n",
        "dt_model = modeling(DecisionTreeClassifier, \"Decision Tree Classifier\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "09QwtF-pFWFO",
        "outputId": "f63cb719-1ccb-4350-ae78-4afacd04720e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Classifier\n",
            "Accuracy: 0.8017037387600567\n",
            "Precision: 0.679814385150812\n",
            "Recall: 0.5104529616724739\n",
            "F1 Score: 0.791997889609071\n"
          ]
        }
      ],
      "source": [
        "from xgboost import XGBClassifier\n",
        "# Running XGBoost Classifier\n",
        "xgb_model = modeling(XGBClassifier, \"XGBoost Classifier\", params={\"use_label_encoder\": False})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fi05FrIVFWFP",
        "outputId": "4fdf2982-6c9a-4841-ed7c-e7ef6c1a9d3e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LightGBM Classifier\n",
            "Accuracy: 0.795551348793185\n",
            "Precision: 0.6599099099099099\n",
            "Recall: 0.5104529616724739\n",
            "F1 Score: 0.7866396687903158\n"
          ]
        }
      ],
      "source": [
        "from lightgbm import LGBMClassifier\n",
        "# Running LightGBM Classifier\n",
        "lgbm_model = modeling(LGBMClassifier, \"LightGBM Classifier\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Improve the best model by tuning hyperparameters\n",
        "# Define the model\n",
        "model = LogisticRegression()\n",
        "\n",
        "# Define evaluation\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "\n",
        "# Define parameters\n",
        "from scipy.stats import loguniform\n",
        "space = dict()\n",
        "space[\"solver\"] = [\"newton-cg\", \"lbfgs\", \"liblinear\", \"saga\"]\n",
        "space[\"penalty\"] = [\"none\", \"l1\", \"l2\", \"elasticnet\"]\n",
        "space[\"C\"] = loguniform(1e-5, 1000)\n",
        "\n",
        "# Define the search\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "search = RandomizedSearchCV(estimator=model, param_distributions=space, n_iter=500, cv=cv, scoring=\"accuracy\", n_jobs=-1, verbose=1)\n",
        "\n",
        "# Fit the search\n",
        "result = search.fit(X_rfe, y)\n",
        "\n",
        "# Summarize the results\n",
        "print(f\"Best Score: {result.best_score_}\")\n",
        "print(f\"Best Parameters: {result.best_params_}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XCQhnXwnLBbw",
        "outputId": "837d3934-b226-43b6-dea3-8617a6b1029c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 30 folds for each of 500 candidates, totalling 15000 fits\n",
            "Best Score: 0.8045796394799054\n",
            "Best Parameters: {'C': 96.14795312015154, 'penalty': 'l2', 'solver': 'newton-cg'}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning:\n",
            "\n",
            "\n",
            "6540 fits failed out of a total of 15000.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "750 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
            "    % (solver, penalty)\n",
            "ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "990 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
            "    % (solver, penalty)\n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "1230 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 459, in _check_solver\n",
            "    solver\n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "930 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1473, in fit\n",
            "    % self.l1_ratio\n",
            "ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "750 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
            "    % (solver, penalty)\n",
            "ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "1080 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 464, in _check_solver\n",
            "    raise ValueError(\"penalty='none' is not supported for the liblinear solver\")\n",
            "ValueError: penalty='none' is not supported for the liblinear solver\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "810 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
            "    % (solver, penalty)\n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning:\n",
            "\n",
            "One or more of the test scores are non-finite: [0.80453236 0.80453236        nan 0.80453236        nan 0.80453236\n",
            " 0.73463008        nan        nan 0.80453236 0.79412167 0.80453236\n",
            "        nan 0.80453236 0.80453236 0.73463008        nan        nan\n",
            " 0.79984701        nan        nan 0.80263969 0.80093562 0.80453236\n",
            " 0.80292338 0.80443746        nan        nan        nan 0.80301801\n",
            " 0.80415377        nan 0.80453236 0.80453229 0.80301828 0.80453236\n",
            "        nan        nan 0.80453236 0.80453236        nan 0.78901065\n",
            "        nan 0.80448488        nan 0.80453236 0.73463008        nan\n",
            " 0.80453236        nan 0.79785871 0.80453236 0.80448501 0.80453236\n",
            " 0.80316026 0.80453236        nan        nan        nan        nan\n",
            " 0.77069397        nan 0.80382287        nan 0.80453236 0.80273412\n",
            " 0.7877305         nan        nan 0.80453229        nan        nan\n",
            " 0.80453236        nan 0.80443746 0.80453236        nan 0.73463008\n",
            "        nan 0.80230832        nan        nan 0.80415357        nan\n",
            " 0.80453236 0.73463008 0.76018704        nan 0.79871145 0.80453236\n",
            " 0.77320096        nan 0.80453236        nan 0.80334965        nan\n",
            " 0.76880057        nan 0.80159803        nan 0.80453236        nan\n",
            "        nan 0.80282909        nan 0.7872098         nan 0.73463008\n",
            "        nan 0.80453236        nan        nan        nan 0.73463008\n",
            " 0.80439018 0.80273419        nan 0.80453236        nan 0.73463008\n",
            " 0.77641945        nan 0.80273426 0.80453236 0.80453236 0.75677943\n",
            " 0.7976227  0.79000376        nan 0.80453236 0.76998355 0.80443766\n",
            " 0.73463008 0.80439025 0.80453236        nan        nan 0.79606101\n",
            " 0.80453236        nan        nan 0.80268704        nan 0.80287624\n",
            "        nan        nan 0.80453236 0.80448494        nan 0.80448488\n",
            " 0.73463008        nan        nan        nan        nan 0.80443766\n",
            " 0.79766978 0.73463008        nan 0.80453236 0.73463008 0.73463008\n",
            " 0.80453236 0.80453236 0.80088814        nan 0.80410642 0.80453229\n",
            " 0.80282902 0.80453236 0.80453236 0.80453236 0.73463008 0.80443753\n",
            " 0.80401159        nan        nan 0.80453236        nan 0.80268704\n",
            " 0.73463008        nan        nan 0.73463008 0.80453236        nan\n",
            "        nan        nan 0.80263963 0.73463008 0.80216634 0.80453236\n",
            "        nan        nan        nan        nan 0.73463008        nan\n",
            " 0.80254486 0.80164531 0.80301801 0.8025448         nan 0.80453236\n",
            " 0.80221369 0.80453236        nan 0.80453236        nan        nan\n",
            " 0.80453236        nan        nan 0.73463008        nan 0.73463008\n",
            " 0.79809585        nan        nan 0.73463008 0.79473559        nan\n",
            " 0.78172019        nan 0.80353898 0.80453236 0.80453236 0.79236917\n",
            "        nan 0.73463008 0.80453236 0.80453236 0.80453236        nan\n",
            " 0.73463008        nan 0.80453236        nan        nan 0.80017845\n",
            " 0.80396444        nan        nan        nan        nan 0.80292358\n",
            "        nan 0.73463008        nan        nan 0.80453236 0.80434283\n",
            "        nan 0.80429548        nan 0.76861125 0.80386982        nan\n",
            " 0.79757542 0.78787261        nan 0.76818565 0.80453236        nan\n",
            "        nan 0.8039175         nan 0.73463008        nan 0.80382247\n",
            " 0.80453236 0.73463008        nan        nan        nan 0.80443759\n",
            " 0.80448501 0.80453236 0.73463008        nan 0.80396458 0.79989436\n",
            "        nan        nan 0.80387002 0.79312755        nan 0.80453236\n",
            " 0.8042482  0.80282902 0.80453236        nan 0.80453236 0.80325529\n",
            " 0.80453236        nan 0.73463008 0.80453236        nan        nan\n",
            "        nan        nan 0.73463008 0.80453236 0.73463008        nan\n",
            " 0.80401159        nan        nan 0.80396458 0.80453236 0.73463008\n",
            "        nan        nan        nan        nan 0.73463008 0.80273439\n",
            " 0.80453236        nan 0.80453236 0.80453236        nan        nan\n",
            "        nan 0.80453236 0.73463008        nan        nan 0.79236917\n",
            "        nan        nan        nan        nan 0.80453236        nan\n",
            "        nan 0.80453236 0.80453229 0.8015038  0.80453236        nan\n",
            " 0.8027814         nan        nan 0.73463008 0.80453236 0.80453236\n",
            "        nan 0.80401179 0.80429555 0.80415357        nan 0.80453236\n",
            "        nan        nan 0.80339673        nan 0.80453236 0.73463008\n",
            "        nan 0.80169299        nan 0.80453236        nan 0.80216634\n",
            " 0.80453236 0.80453236        nan        nan        nan 0.80292365\n",
            "        nan        nan 0.80453236        nan 0.78044212 0.80453236\n",
            "        nan 0.80453236        nan        nan 0.80453236        nan\n",
            " 0.80453236 0.73463008 0.80453229        nan        nan 0.80301741\n",
            "        nan 0.80453236        nan 0.80429548        nan        nan\n",
            " 0.73463008 0.80453236 0.80386975 0.80453236 0.80453236 0.73463008\n",
            "        nan        nan 0.80453229 0.73463008        nan        nan\n",
            " 0.78153248        nan        nan        nan        nan        nan\n",
            " 0.80453236 0.80453236 0.73463008        nan        nan 0.80453236\n",
            "        nan 0.80453236 0.80453236        nan        nan 0.80443759\n",
            " 0.80453236 0.80453236 0.73463008        nan        nan        nan\n",
            " 0.80453236        nan 0.73463008        nan        nan 0.79246427\n",
            "        nan 0.80453236 0.80453236 0.79984701        nan 0.80145592\n",
            " 0.73463008 0.73463008        nan 0.80453236        nan        nan\n",
            " 0.73463008        nan 0.80453236 0.80443759        nan 0.80453236\n",
            " 0.80453236        nan 0.73463008        nan 0.80453236 0.80453236\n",
            " 0.80453236 0.73463008        nan        nan        nan 0.80405894\n",
            " 0.73463008        nan 0.73463008 0.73463008        nan 0.73463008\n",
            " 0.78815838        nan        nan 0.80457964 0.80453236 0.80448501\n",
            "        nan 0.73463008        nan 0.73463008 0.80453236        nan\n",
            " 0.73463008        nan        nan        nan        nan        nan\n",
            " 0.77414746 0.80453236 0.80453236        nan        nan 0.76936815\n",
            " 0.80453236        nan]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "params = result.best_params_\n",
        "params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tp-EGy1xVkWp",
        "outputId": "9c4bffcb-7a94-4fc6-8f0a-83053d74c112"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 96.14795312015154, 'penalty': 'l2', 'solver': 'newton-cg'}"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Improving the Logistic Regression model\n",
        "log_model = modeling(LogisticRegression, 'Logistic Regression Classification', params=params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v71KXvMCZSyE",
        "outputId": "23efe5a1-cf7f-43b6-fc3f-949c19b3ce5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Classification\n",
            "Accuracy: 0.8116422148603881\n",
            "Precision: 0.6825726141078838\n",
            "Recall: 0.573170731707317\n",
            "F1 Score: 0.8061705031280859\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "#Saving best model \n",
        "filename = 'model.sav'\n",
        "joblib.dump(log_model, filename)"
      ],
      "metadata": {
        "id": "0r1IXVwRckCq",
        "outputId": "aff8cfb8-50cf-4ced-cb4e-c7f12ec19256",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model.sav']"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    }
  ],
  "metadata": {
    "interpreter": {
      "hash": "483abfc7fdcd927bfa336910f494643cce94b3dfa36bcded073270b8df64edb3"
    },
    "kernelspec": {
      "display_name": "Python 3.9.6 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.6"
    },
    "orig_nbformat": 4,
    "colab": {
      "name": "EDA.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "TOUxF2cBFWFF",
        "YfGPOwUZFWFG",
        "ASKLbHnDFWFG",
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        "m-mkxmF9FWFJ",
        "urRm3NW0FWFL",
        "5EPZyknzFWFL"
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    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}