Random_Forest_and_Logistic_Regression.ipynb

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        "## Objective\n",
        "\n",
        "The primary objective of this project is to develop a robust classification model that will facilitate our client, AT&C, in accurately predicting the likelihood of customer churn. By leveraging technical and managerial insights gained from this model, our client will be empowered to proactively prevent customer churn, thereby ensuring the retention of both current and future customers. This strategic approach will not only drive revenue but also foster business growth for our client."
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        "## Method of Analysis\n",
        "\n",
        "Our core method of analysis is the Logistic Regression model. In general we sequence our implementation by leveraging the data mining process in the following order:\n",
        "\n",
        "- Data preprocessing and Exploration: This involves the use of proper summary descriptive statistics and visualizations to understand the distribution of individual variables and relationship between two or more variables; duplicates analysis, outlier detection and or removal, missing values analysis, computation of new variables if necessary etc.\n",
        "- Dimension reduction: selection of a subset of variables for model building.\n",
        "- Data Partitioning: Creating Test and training dataset for K-fold Cross-Validation to evaluate model performance.\n",
        "- Model Building: building the logistic regression classification model\n",
        "- Model Evaluation: computation of metrics such as accuracy, sensitivity, specificity, precision etc.\n",
        "- Model Deployment"
      ]
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        "## Data Description\n",
        "\n",
        "Originated from India, the dataset contains 13000 rows of customer data across 14 variables. The dataset includes various demographic, location, and usage pattern variables for each customer, as well as a binary variable indicating whether the customer has churned or not. We describe the data variables below:\n",
        "\n",
        "### Dependent Variable\n",
        "- churn: Binary variable indicating whether the customer has churned or not (1 = churned, 0 = not churned).\n",
        "\n",
        "### Independent Variables\n",
        "- customer_id: Unique identifier for each customer.\n",
        "- gender: The gender of the customer.\n",
        "- telecom_partner: The telecom partner associated with the customer.\n",
        "- age: The age of the customer.\n",
        "- state: The Indian state in which the customer is located.\n",
        "- city: The city in which the customer is located.\n",
        "- pincode: The pincode of the customer's location.\n",
        "- date_of_registration: The date on which the customer registered with the telecom partner.\n",
        "- num_dependents: The number of dependents (e.g. children) the customer has.\n",
        "- estimated_salary: The customer's estimated salary.\n",
        "- calls_made: The number of calls made by the customer.\n",
        "- sms_sent: The number of SMS messages sent by the customer.\n",
        "- data_used: The amount of data used by the customer."
      ]
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      "source": [
        "## Duplicate Analysis\n",
        "In the next steps we implement analysis to solve for and manage any duplicates in the dataset"
      ]
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            "Starting Spark application\n"
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            "SparkSession available as 'spark'.\n"
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            "   customer_id telecom_partner gender  age    state       city  pincode date_of_registration  num_dependents  estimated_salary  calls_made  sms_sent  data_used  churn\n",
            "0       241859        Vodafone      M   58  Manipur  Bangalore   500407           2023-04-25               2             41014          61        45       9961      1\n",
            "1        17876          Airtel      M   59  Mizoram     Mumbai   507230           2020-03-30               0             92175          92        12       1140      0\n",
            "2        38273            BSNL      M   40    Bihar    Chennai   453495           2020-07-10               2            109436          82        19       -604      0\n",
            "/mnt1/yarn/usercache/livy/appcache/application_1690843046081_0006/container_1690843046081_0006_01_000001/pyspark.zip/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n",
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        "# first we provision the required libraries to import our dataset below\n",
        "import pyspark.pandas as ps\n",
        "import numpy as np\n",
        "data = ps.read_csv('s3://jiogboles3private/spark/new_telcom_churn.csv')\n",
        "data.head(3)"
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            "(13000, 14)"
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        "# Here we'd like to confirm the dimension i.e number of rows and columns in the dataset after dropping the customerID\n",
        "# column\n",
        "data.shape"
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        "As seen above, the code output confirms that we have 13000 observations and 14 variables"
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          "text": [
            "13000 13000\n",
            "/mnt1/yarn/usercache/livy/appcache/application_1690843046081_0006/container_1690843046081_0006_01_000001/pyspark.zip/pyspark/pandas/utils.py:975: PandasAPIOnSparkAdviceWarning: If `index_col` is not specified for `to_spark`, the existing index is lost when converting to Spark DataFrame.\n",
            "  warnings.warn(message, PandasAPIOnSparkAdviceWarning)"
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      "source": [
        "# Next we run the code to check if there are any duplicates in the dataset\n",
        "print(data.shape[0], data.to_spark().distinct().count())"
      ]
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      "source": [
        "The output of the code above shows that all rows of the dataset are unique, as such there are not duplicates in the dataset."
      ]
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      "source": [
        "## Missing Values Analysis\n",
        "\n",
        "Next we check for missing or NA values in the dataset."
      ]
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            "0"
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      "source": [
        "#We run the following code to check if there are any empty or null values in the dataset\n",
        "x = data.isna().sum(axis=1)\n",
        "x[x >= 1].shape[0]"
      ]
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        "The output of the code above returns a count of 0, implying that there are zero i.e no missing values in the dataset. Let's continue..."
      ]
    },
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      "source": [
        "## Descriptive Statistics\n",
        "\n",
        "Next we generate descriptive statistics below:"
      ]
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          "text": [
            "         customer_id          age        pincode        date_of_registration  num_dependents  estimated_salary    calls_made      sms_sent     data_used         churn\n",
            "count   13000.000000  13000.00000   13000.000000                       13000     13000.00000      13000.000000  13000.000000  13000.000000  13000.000000  13000.000000\n",
            "mean   122449.000000     46.00000  544247.000000  2021-09-04 08:49:35.261538         1.00000      85388.000000     48.000000     23.000000   5033.000000      0.000000\n",
            "min         6.000000     18.00000  100020.000000         2020-01-01 00:00:00         0.00000      20004.000000    -10.000000     -5.000000   -955.000000      0.000000\n",
            "25%     62181.000000     32.00000  319563.000000         2020-11-07 00:00:00         1.00000      53154.000000     24.000000     11.000000   2515.000000      0.000000\n",
            "50%    122235.000000     46.00000  541106.000000         2021-09-03 00:00:00         2.00000      85251.000000     49.000000     24.000000   5043.000000      0.000000\n",
            "75%    183603.000000     61.00000  767513.000000         2022-07-07 00:00:00         3.00000     118089.000000     73.000000     36.000000   7576.000000      0.000000\n",
            "max    243550.000000     74.00000  999872.000000         2023-05-03 00:00:00         4.00000     149997.000000    108.000000     53.000000  10925.000000      1.000000\n",
            "std     70289.169404     16.47206  259782.402896                        None         1.41871      37520.533624     29.239086     14.647663   2948.486764      0.400015"
          ]
        }
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      "source": [
        "#we run the code below to generate descriptive stats\n",
        "descriptive_stats = data.describe()\n",
        "descriptive_stats"
      ]
    },
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      "source": [
        "Overall the descriptive statistics seem relatively regular with a few numbers appearing to standout as potential outliers liers or error imputs. for instance the minimum of the \"calls_made\" is a negative number which is not intuitive in this context. It's not possible to make negative amount of calls. we'll further investigate in the outlier section."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6be74f66",
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      "source": [
        "## Visualization\n",
        "Next, we visualize the dataset for categorical and numerical variables."
      ]
    },
    {
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      "source": [
        "### Numerical Variables Visualization\n",
        "For numerical variables, we generate histograms using the process below:"
      ]
    },
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      "source": [
        "#first we write the code below that defines a function to plot histograms for each numerical variable in the dataset\n",
        "def plot_hist(data, features, n_rows, n_cols):\n",
        "    fig, ax = plt.subplots(n_rows, n_cols)\n",
        "    fig.tight_layout(pad=2.2)\n",
        "    for i in range(len(features)):\n",
        "        data[features[i]].hist(ax=ax[i//n_cols, i%n_cols], title=features[i])"
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        "# The code below provisions the required libraries for visualization\n",
        "import matplotlib.pyplot as plt\n",
        "ps.set_option('plotting.backend', 'matplotlib')"
      ]
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# In the code below we create a variable list of the numerical variables and call the function on the list to create\n",
        "# the histogram for each variable\n",
        "features = ['age', 'num_dependents','estimated_salary','calls_made', 'data_used', 'sms_sent']\n",
        "plot_hist(data=data, features=features, n_rows=2, n_cols=3)\n",
        "%matplot plt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "676a5ef6",
      "metadata": {
        "id": "676a5ef6"
      },
      "source": [
        "### Visualizing Interaction between numerical variables using Scatter Plots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6b995f7f",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "28e2f771b5804758861ad33e861d90ea",
            ""
          ]
        },
        "id": "6b995f7f",
        "outputId": "dbfa4528-f214-48f6-b23e-4f4a3d2f9496"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "28e2f771b5804758861ad33e861d90ea",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#we run the code below to visualize the relationship between the no of calls made and amount of data used\n",
        "data.plot.scatter('calls_made', 'data_used')\n",
        "%matplot plt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "61a60e1f",
      "metadata": {
        "id": "61a60e1f"
      },
      "source": [
        "The scatterplot above shows a clustering of certain points which might suggest that type of association between the variables. however the relationship as can be seen does not seeem to follow any positive or negative pattern"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3c23699d",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "e5798ecfeae3445d87af547d7d5df291",
            ""
          ]
        },
        "id": "3c23699d",
        "outputId": "f93ae90d-21e3-47e2-d390-be4669caf28c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e5798ecfeae3445d87af547d7d5df291",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#we run the code below to visualize the relationship between the no of data_used and sms_sent\n",
        "data.plot.scatter('data_used', 'sms_sent')\n",
        "%matplot plt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eec8ce45",
      "metadata": {
        "id": "eec8ce45"
      },
      "source": [
        "The scatterplot above shows a clustering of certain points which might suggest that type of association between the variables. however the relationship as can be seen does not seeem to follow any positive or negative pattern"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "51d417bd",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "0f355f47289743a396e0edb6df5e99b7",
            ""
          ]
        },
        "id": "51d417bd",
        "outputId": "33e440c0-1a82-488c-cd9a-670b869f2b53"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0f355f47289743a396e0edb6df5e99b7",
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#we run the code below to visualize the relationship between age and estimated salary\n",
        "data.plot.scatter('age', 'estimated_salary')\n",
        "%matplot plt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3c719119",
      "metadata": {
        "id": "3c719119"
      },
      "source": [
        "The scatterplot above shows a clustering of certain points which might suggest that type of association between the variables. however the relationship as can be seen does not seeem to follow any positive or negative pattern"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af96b9dd",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "2d2accf9d8554606acff473b30f11fa9",
            ""
          ]
        },
        "id": "af96b9dd",
        "outputId": "f0883f05-4e2a-449e-b74d-6d20ab2f978c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2d2accf9d8554606acff473b30f11fa9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#we run the code below to visualize the relationship between the no of calls made and sms_sent\n",
        "data.plot.scatter('calls_made', 'sms_sent')\n",
        "%matplot plt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "95cf2ae1",
      "metadata": {
        "id": "95cf2ae1"
      },
      "source": [
        "The scatterplot above shows a clustering of certain points which might suggest that type of association between the variables. however the relationship as can be seen does not seeem to follow any positive or negative pattern"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bad76db9",
      "metadata": {
        "id": "bad76db9"
      },
      "source": [
        "### Categorical Variables Visualization\n",
        "\n",
        "Below we generate barcharts for the categorical variables using the process below"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6ec98a5d",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "d89dda4fdacc483ba77f44e50adfe15f",
            ""
          ]
        },
        "id": "6ec98a5d",
        "outputId": "80c05a67-bb81-4580-b5ce-7e22aa09adff"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d89dda4fdacc483ba77f44e50adfe15f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#we write the code below to define a function to create bar charts of the categorical variables\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_barchart(data, column):\n",
        "    value_counts = data[column].value_counts()\n",
        "    plt.figure(figsize=(12, 6))  # Adjust the figure size as needed\n",
        "    ax = value_counts.plot(kind='bar')\n",
        "    ax.set_xlabel(column)\n",
        "    ax.set_ylabel('Count')\n",
        "    ax.set_title(f'Bar Chart of {column}')\n",
        "    plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a76f628b",
      "metadata": {
        "scrolled": true,
        "colab": {
          "referenced_widgets": [
            "480c616d3fa1449ba64c1fb70ae6d545",
            ""
          ]
        },
        "id": "a76f628b",
        "outputId": "e05ba4fe-5d2c-458c-c11d-b43ffe043f85"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "480c616d3fa1449ba64c1fb70ae6d545",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#we run the code below to create a barchart for telecom_partner variable\n",
        "plot_barchart(data, 'telecom_partner')\n",
        "%matplot plt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ece142be",
      "metadata": {
        "id": "ece142be"
      },
      "source": [
        "The barchart above shows a more or less even distribution of the the four telecom partners"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "60f89420",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "59876aaa665a4955b82de1b5d16c2aea",
            ""
          ]
        },
        "id": "60f89420",
        "outputId": "39d45282-3bc9-480e-9472-9772fe65c569"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "59876aaa665a4955b82de1b5d16c2aea",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
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          "output_type": "display_data"
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#we run the code below to create a barchart for gender variable\n",
        "plot_barchart(data, 'gender')\n",
        "%matplot plt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3166df0b",
      "metadata": {
        "id": "3166df0b"
      },
      "source": [
        "The barchart shows that there are more male than female represented in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "046f7ea9",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "6593cd521e364d61b469e0483e213744",
            ""
          ]
        },
        "id": "046f7ea9",
        "outputId": "3a5622a7-fd30-4ee2-8687-c0ba7374efef"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6593cd521e364d61b469e0483e213744",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#we run the code below to create a barchart for state variable\n",
        "plot_barchart(data, 'state')\n",
        "%matplot plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ec662515",
      "metadata": {
        "scrolled": true,
        "colab": {
          "referenced_widgets": [
            "348f3062e3a84d5ebd8dc3766bbb419f",
            ""
          ]
        },
        "id": "ec662515",
        "outputId": "20d2facb-1717-477d-cd7d-44d8ffa8d2a9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "348f3062e3a84d5ebd8dc3766bbb419f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#we run the code below to create a barchart for city variable\n",
        "plot_barchart(data, 'city')\n",
        "%matplot plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a26f7d1b",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "05b6f5c492564054981c4e3a07e0e1b7",
            ""
          ]
        },
        "id": "a26f7d1b",
        "outputId": "986e81da-ac8f-4526-90db-5fe040d3f40a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "05b6f5c492564054981c4e3a07e0e1b7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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H6HsrPQKwFvMvGVTpEQAA4GPlDiwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AKohHHnkkX/jCF1JXV5eqqqrcddddTfaXSqWMHTs2W2+9dTp27JgBAwbkxRdfbLJm0aJFGTJkSKqrq9O5c+cMHTo0b775ZpM1zzzzTPbbb7906NAh3bt3z/jx41eb5Re/+EV69eqVDh06ZOedd86vfvWrDX69AAAAAB+WgFUQb731VnbZZZdcd911a9w/fvz4XHPNNZkwYUIef/zxbLLJJhk4cGDefffd8pohQ4Zkzpw5mTp1aiZPnpxHHnkkw4YNK+9vbGzMIYcckm233TazZs3KpZdemvPPPz833nhjec1jjz2Wr3zlKxk6dGh+97vf5cgjj8yRRx6Z55577uO7eAAAAIC1qCqVSqVKD0FTVVVVufPOO3PkkUcm+dvdV3V1dTnttNNy+umnJ0kaGhpSW1ubiRMnZvDgwXn++efTp0+fPPHEE9ljjz2SJFOmTMnhhx+eP/3pT6mrq8sNN9yQs88+O/X19WnXrl2SZPTo0bnrrrsyd+7cJMmXv/zlvPXWW5k8eXJ5nr333ju77rprJkyY8KHmb2xsTE1NTRoaGlJdXb2hvi1UUI/R91Z6BGAt5l8yqNIjAABUlN9DWz53YDUD8+bNS319fQYMGFDeVlNTk7322iszZsxIksyYMSOdO3cux6skGTBgQFq1apXHH3+8vGb//fcvx6skGThwYF544YW88cYb5TXvP8+qNavOAwAAALCxtan0APxj9fX1SZLa2tom22tra8v76uvr07Vr1yb727Rpk80337zJmp49e652jFX7unTpkvr6+rWeZ02WLl2apUuXlt83Njauy+UBAAAArJU7sFhv48aNS01NTfnVvXv3So8EAAAAtCACVjPQrVu3JMmCBQuabF+wYEF5X7du3bJw4cIm+997770sWrSoyZo1HeP95/igNav2r8mYMWPS0NBQfr3yyivreokAAAAAH0jAagZ69uyZbt26Zdq0aeVtjY2Nefzxx9OvX78kSb9+/bJ48eLMmjWrvGb69OlZuXJl9tprr/KaRx55JMuXLy+vmTp1anbYYYd06dKlvOb951m1ZtV51qR9+/aprq5u8gIAAADYUASsgnjzzTcze/bszJ49O8nfHtw+e/bsvPzyy6mqqsrIkSNz0UUX5e67786zzz6br371q6mrqyv/pcLevXvn0EMPzUknnZSZM2fm0UcfzYgRIzJ48ODU1dUlSY499ti0a9cuQ4cOzZw5c3L77bfn6quvzqhRo8pzfOc738mUKVNy+eWXZ+7cuTn//PPz5JNPZsSIERv7WwIAAACQxEPcC+PJJ59M//79y+9XRaUTTjghEydOzJlnnpm33norw4YNy+LFi7PvvvtmypQp6dChQ/lrbr311owYMSIHH3xwWrVqlaOPPjrXXHNNeX9NTU0eeOCBDB8+PH379s2WW26ZsWPHZtiwYeU1++yzTyZNmpRzzjkn3/3ud/OZz3wmd911V3baaaeN8F0AAAAAWF1VqVQqVXoIWpbGxsbU1NSkoaHBxwlbiB6j7630CMBazL9kUKVHAACoKL+Htnw+QggAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagAUAAABAoQlYAAAAABSagNVMrFixIueee2569uyZjh07ZrvttsuFF16YUqlUXlMqlTJ27NhsvfXW6dixYwYMGJAXX3yxyXEWLVqUIUOGpLq6Op07d87QoUPz5ptvNlnzzDPPZL/99kuHDh3SvXv3jB8/fqNcIwAAAMCaCFjNxPe///3ccMMNufbaa/P888/n+9//fsaPH58f/OAH5TXjx4/PNddckwkTJuTxxx/PJptskoEDB+bdd98trxkyZEjmzJmTqVOnZvLkyXnkkUcybNiw8v7GxsYccsgh2XbbbTNr1qxceumlOf/883PjjTdu1OsFAAAAWKWq9P5beCisI444IrW1tfnxj39c3nb00UenY8eO+elPf5pSqZS6urqcdtppOf3005MkDQ0Nqa2tzcSJEzN48OA8//zz6dOnT5544onsscceSZIpU6bk8MMPz5/+9KfU1dXlhhtuyNlnn536+vq0a9cuSTJ69OjcddddmTt37oeatbGxMTU1NWloaEh1dfUG/k5QCT1G31vpEYC1mH/JoEqPAABQUX4PbfncgdVM7LPPPpk2bVr+53/+J0ny9NNP57//+79z2GGHJUnmzZuX+vr6DBgwoPw1NTU12WuvvTJjxowkyYwZM9K5c+dyvEqSAQMGpFWrVnn88cfLa/bff/9yvEqSgQMH5oUXXsgbb7zxsV8nAAAAwN9rU+kB+HBGjx6dxsbG9OrVK61bt86KFSty8cUXZ8iQIUmS+vr6JEltbW2Tr6utrS3vq6+vT9euXZvsb9OmTTbffPMma3r27LnaMVbt69Kly2qzLV26NEuXLi2/b2xsXJ9LBQAAAGjCHVjNxM9//vPceuutmTRpUp566qnccsstueyyy3LLLbdUerSMGzcuNTU15Vf37t0rPRIAAADQgghYzcQZZ5yR0aNHZ/Dgwdl5551z/PHH59RTT824ceOSJN26dUuSLFiwoMnXLViwoLyvW7duWbhwYZP97733XhYtWtRkzZqO8f5z/L0xY8akoaGh/HrllVfW82oBAAAA/h8Bq5l4++2306pV0/9crVu3zsqVK5MkPXv2TLdu3TJt2rTy/sbGxjz++OPp169fkqRfv35ZvHhxZs2aVV4zffr0rFy5MnvttVd5zSOPPJLly5eX10ydOjU77LDDGj8+mCTt27dPdXV1kxcAAADAhiJgNRNf+MIXcvHFF+fee+/N/Pnzc+edd+aKK67Iv/3bvyVJqqqqMnLkyFx00UW5++678+yzz+arX/1q6urqcuSRRyZJevfunUMPPTQnnXRSZs6cmUcffTQjRozI4MGDU1dXlyQ59thj065duwwdOjRz5szJ7bffnquvvjqjRo2q1KUDAAAAn3Ae4t5M/OAHP8i5556b//W//lcWLlyYurq6nHzyyRk7dmx5zZlnnpm33norw4YNy+LFi7PvvvtmypQp6dChQ3nNrbfemhEjRuTggw9Oq1atcvTRR+eaa64p76+pqckDDzyQ4cOHp2/fvtlyyy0zduzYDBs2bKNeLwAAAMAqVaVSqVTpIWhZGhsbU1NTk4aGBh8nbCF6jL630iMAazH/kkGVHgEAoKL8Htry+QghAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAIWAAAAAIUmYAEAAABQaAJWM/LnP/85xx13XLbYYot07NgxO++8c5588sny/lKplLFjx2brrbdOx44dM2DAgLz44otNjrFo0aIMGTIk1dXV6dy5c4YOHZo333yzyZpnnnkm++23Xzp06JDu3btn/PjxG+X6AAAAANZEwGom3njjjXz+859P27Ztc9999+X3v/99Lr/88nTp0qW8Zvz48bnmmmsyYcKEPP7449lkk00ycODAvPvuu+U1Q4YMyZw5czJ16tRMnjw5jzzySIYNG1be39jYmEMOOSTbbrttZs2alUsvvTTnn39+brzxxo16vQAAAACrVJVKpVKlh+AfGz16dB599NH85je/WeP+UqmUurq6nHbaaTn99NOTJA0NDamtrc3EiRMzePDgPP/88+nTp0+eeOKJ7LHHHkmSKVOm5PDDD8+f/vSn1NXV5YYbbsjZZ5+d+vr6tGvXrnzuu+66K3Pnzv1QszY2NqampiYNDQ2prq7eAFdPpfUYfW+lRwDWYv4lgyo9AgBARfk9tOVzB1Yzcffdd2ePPfbIv//7v6dr167Zbbfd8sMf/rC8f968eamvr8+AAQPK22pqarLXXntlxowZSZIZM2akc+fO5XiVJAMGDEirVq3y+OOPl9fsv//+5XiVJAMHDswLL7yQN954Y42zLV26NI2NjU1eAAAAABuKgNVMvPTSS7nhhhvymc98Jvfff3++9a1v5dvf/nZuueWWJEl9fX2SpLa2tsnX1dbWlvfV19ena9euTfa3adMmm2++eZM1azrG+8/x98aNG5eampryq3v37ut5tQAAAAD/j4DVTKxcuTK77757/uM//iO77bZbhg0blpNOOikTJkyo9GgZM2ZMGhoayq9XXnml0iMBAAAALYiA1UxsvfXW6dOnT5NtvXv3zssvv5wk6datW5JkwYIFTdYsWLCgvK9bt25ZuHBhk/3vvfdeFi1a1GTNmo7x/nP8vfbt26e6urrJCwAAAGBDEbCaic9//vN54YUXmmz7n//5n2y77bZJkp49e6Zbt26ZNm1aeX9jY2Mef/zx9OvXL0nSr1+/LF68OLNmzSqvmT59elauXJm99tqrvOaRRx7J8uXLy2umTp2aHXbYoclfPAQAAADYWASsZuLUU0/Nb3/72/zHf/xH/vCHP2TSpEm58cYbM3z48CRJVVVVRo4cmYsuuih33313nn322Xz1q19NXV1djjzyyCR/u2Pr0EMPzUknnZSZM2fm0UcfzYgRIzJ48ODU1dUlSY499ti0a9cuQ4cOzZw5c3L77bfn6quvzqhRoyp16QAAAMAnnIC1nv7pn/4pf/3rX1fbvnjx4vzTP/3TBjvPnnvumTvvvDM/+9nPstNOO+XCCy/MVVddlSFDhpTXnHnmmTnllFMybNiw7LnnnnnzzTczZcqUdOjQobzm1ltvTa9evXLwwQfn8MMPz7777psbb7yxvL+mpiYPPPBA5s2bl759++a0007L2LFjM2zYsA12LQAAAADroqpUKpUqPURz1qpVqzX+db8FCxbk05/+dJYuXVqhySqnsbExNTU1aWho8DysFqLH6HsrPQKwFvMvGVTpEQAAKsrvoS1fm0oP0Fzdfffd5X/ff//9qampKb9fsWJFpk2blh49elRgMgAAAICWRcD6iFY9V6qqqionnHBCk31t27ZNjx49cvnll1dgMgAAAICWRcD6iFauXJnkb3/974knnsiWW25Z4YkAAAAAWiYBaz3Nmzev0iMAAAAAtGgC1gYwbdq0TJs2LQsXLizfmbXKTTfdVKGpAAAAAFoGAWs9fe9738sFF1yQPfbYI1tvvXWqqqoqPRIAAABAiyJgracJEyZk4sSJOf744ys9CgAAAECL1KrSAzR3y5Ytyz777FPpMQAAAABaLAFrPZ144omZNGlSpccAAAAAaLF8hHA9vfvuu7nxxhvz61//Op/97GfTtm3bJvuvuOKKCk0GAAAA0DIIWOvpmWeeya677pokee6555rs80B3AAAAgPUnYK2nBx98sNIjAAAAALRonoEFAAAAQKG5A2s99e/ff60fFZw+ffpGnAYAAACg5RGw1tOq51+tsnz58syePTvPPfdcTjjhhMoMBQAAANCCCFjr6corr1zj9vPPPz9vvvnmRp4GAAAAoOXxDKyPyXHHHZebbrqp0mMAAAAANHsC1sdkxowZ6dChQ6XHAAAAAGj2fIRwPR111FFN3pdKpfzlL3/Jk08+mXPPPbdCUwEAAAC0HALWeqqpqWnyvlWrVtlhhx1ywQUX5JBDDqnQVAAAAAAth4C1nm6++eZKjwAAAADQoglYG8isWbPy/PPPJ0l23HHH7LbbbhWeCAAAAKBlELDW08KFCzN48OA89NBD6dy5c5Jk8eLF6d+/f2677bZstdVWlR0QAAAAoJnzVwjX0ymnnJIlS5Zkzpw5WbRoURYtWpTnnnsujY2N+fa3v13p8QAAAACaPXdgracpU6bk17/+dXr37l3e1qdPn1x33XUe4g4AAACwAbgDaz2tXLkybdu2XW1727Zts3LlygpMBAAAANCyCFjr6aCDDsp3vvOdvPrqq+Vtf/7zn3Pqqafm4IMPruBkAAAAAC2DgLWerr322jQ2NqZHjx7Zbrvtst1226Vnz55pbGzMD37wg0qPBwAAANDseQbWeurevXueeuqp/PrXv87cuXOTJL17986AAQMqPBkAAABAy+AOrI9o+vTp6dOnTxobG1NVVZV/+Zd/ySmnnJJTTjkle+65Z3bcccf85je/qfSYAAAAAM2egPURXXXVVTnppJNSXV292r6ampqcfPLJueKKKyowGQAAAEDLImB9RE8//XQOPfTQD9x/yCGHZNasWRtxIgAAAICWScD6iBYsWJC2bdt+4P42bdrktdde24gTAQAAALRMAtZHtM022+S55577wP3PPPNMtt566404EQAAAEDLJGB9RIcffnjOPffcvPvuu6vte+edd3LeeefliCOOqMBkAAAAAC1Lm0oP0Fydc845ueOOO/LP//zPGTFiRHbYYYckydy5c3PddddlxYoVOfvssys8JQAAAEDzJ2B9RLW1tXnsscfyrW99K2PGjEmpVEqSVFVVZeDAgbnuuutSW1tb4SkBAAAAmj8Baz1su+22+dWvfpU33ngjf/jDH1IqlfKZz3wmXbp0qfRoAAAAAC2GgLUBdOnSJXvuuWelxwAAAABokTzEHQAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBq5m65JJLUlVVlZEjR5a3vfvuuxk+fHi22GKLbLrppjn66KOzYMGCJl/38ssvZ9CgQenUqVO6du2aM844I++9916TNQ899FB23333tG/fPttvv30mTpy4Ea4IAAAAYM0ErGboiSeeyP/+3/87n/3sZ5tsP/XUU3PPPffkF7/4RR5++OG8+uqrOeqoo8r7V6xYkUGDBmXZsmV57LHHcsstt2TixIkZO3Zsec28efMyaNCg9O/fP7Nnz87IkSNz4okn5v77799o1wcAAADwfgJWM/Pmm29myJAh+eEPf5guXbqUtzc0NOTHP/5xrrjiihx00EHp27dvbr755jz22GP57W9/myR54IEH8vvf/z4//elPs+uuu+awww7LhRdemOuuuy7Lli1LkkyYMCE9e/bM5Zdfnt69e2fEiBE55phjcuWVV1bkegEAAAAErGZm+PDhGTRoUAYMGNBk+6xZs7J8+fIm23v16pVPf/rTmTFjRpJkxowZ2XnnnVNbW1teM3DgwDQ2NmbOnDnlNX9/7IEDB5aPAQAAALCxtan0AHx4t912W5566qk88cQTq+2rr69Pu3bt0rlz5ybba2trU19fX17z/ni1av+qfWtb09jYmHfeeScdO3Zc7dxLly7N0qVLy+8bGxvX/eIAAAAAPoA7sJqJV155Jd/5zndy6623pkOHDpUep4lx48alpqam/OrevXulRwIAAABaEAGrmZg1a1YWLlyY3XffPW3atEmbNm3y8MMP55prrkmbNm1SW1ubZcuWZfHixU2+bsGCBenWrVuSpFu3bqv9VcJV7//Rmurq6jXefZUkY8aMSUNDQ/n1yiuvbIhLBgAAAEgiYDUbBx98cJ599tnMnj27/Npjjz0yZMiQ8r/btm2badOmlb/mhRdeyMsvv5x+/folSfr165dnn302CxcuLK+ZOnVqqqur06dPn/Ka9x9j1ZpVx1iT9u3bp7q6uskLAAAAYEPxDKxmYrPNNstOO+3UZNsmm2ySLbbYorx96NChGTVqVDbffPNUV1fnlFNOSb9+/bL33nsnSQ455JD06dMnxx9/fMaPH5/6+vqcc845GT58eNq3b58k+eY3v5lrr702Z555Zr7xjW9k+vTp+fnPf5577713414wAAAAwP9PwGpBrrzyyrRq1SpHH310li5dmoEDB+b6668v72/dunUmT56cb33rW+nXr1822WSTnHDCCbngggvKa3r27Jl77703p556aq6++up86lOfyo9+9KMMHDiwEpcEAAAAkKpSqVSq9BC0LI2NjampqUlDQ4OPE7YQPUa7Aw+KbP4lgyo9AgBARfk9tOXzDCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACq1NpQcAAICWqsfoeys9ArAW8y8ZVOkRgA/JHVgAAAAAFJqABQAAAEChCVgAAAAAFJqABQAAAEChCVgAAAAAFJqABQAAAEChCVgAAAAAFJqABQAAAEChCVgAAAAAFJqABQAAAEChCVgAAAAAFJqABQAAAEChCVgAAAAAFJqABQAAAEChCVgAAAAAFJqABQAAAEChCVgAAAAAFJqABQAAAEChCVgAAAAAFJqABQAAAEChCVjNxLhx47Lnnntms802S9euXXPkkUfmhRdeaLLm3XffzfDhw7PFFltk0003zdFHH50FCxY0WfPyyy9n0KBB6dSpU7p27Zozzjgj7733XpM1Dz30UHbfffe0b98+22+/fSZOnPhxXx4AAADABxKwmomHH344w4cPz29/+9tMnTo1y5cvzyGHHJK33nqrvObUU0/NPffck1/84hd5+OGH8+qrr+aoo44q71+xYkUGDRqUZcuW5bHHHsstt9ySiRMnZuzYseU18+bNy6BBg9K/f//Mnj07I0eOzIknnpj7779/o14vAAAAwCpVpVKpVOkhWHevvfZaunbtmocffjj7779/GhoastVWW2XSpEk55phjkiRz585N7969M2PGjOy999657777csQRR+TVV19NbW1tkmTChAk566yz8tprr6Vdu3Y566yzcu+99+a5554rn2vw4MFZvHhxpkyZ8qFma2xsTE1NTRoaGlJdXb3hL56Nrsfoeys9ArAW8y8ZVOkRgA/gZygUm5+hLYffQ1s+d2A1Uw0NDUmSzTffPEkya9asLF++PAMGDCiv6dWrVz796U9nxowZSZIZM2Zk5513LserJBk4cGAaGxszZ86c8pr3H2PVmlXHWJOlS5emsbGxyQsAAABgQxGwmqGVK1dm5MiR+fznP5+ddtopSVJfX5927dqlc+fOTdbW1tamvr6+vOb98WrV/lX71ramsbEx77zzzhrnGTduXGpqasqv7t27r/c1AgAAAKwiYDVDw4cPz3PPPZfbbrut0qMkScaMGZOGhoby65VXXqn0SAAAAEAL0qbSA7BuRowYkcmTJ+eRRx7Jpz71qfL2bt26ZdmyZVm8eHGTu7AWLFiQbt26ldfMnDmzyfFW/ZXC96/5+79cuGDBglRXV6djx45rnKl9+/Zp3779el8bAAAAwJq4A6uZKJVKGTFiRO68885Mnz49PXv2bLK/b9++adu2baZNm1be9sILL+Tll19Ov379kiT9+vXLs88+m4ULF5bXTJ06NdXV1enTp095zfuPsWrNqmMAAAAAbGzuwGomhg8fnkmTJuWXv/xlNttss/Izq2pqatKxY8fU1NRk6NChGTVqVDbffPNUV1fnlFNOSb9+/bL33nsnSQ455JD06dMnxx9/fMaPH5/6+vqcc845GT58ePkOqm9+85u59tprc+aZZ+Yb3/hGpk+fnp///Oe5915/QQcAAACoDHdgNRM33HBDGhoacuCBB2brrbcuv26//fbymiuvvDJHHHFEjj766Oy///7p1q1b7rjjjvL+1q1bZ/LkyWndunX69euX4447Ll/96ldzwQUXlNf07Nkz9957b6ZOnZpddtkll19+eX70ox9l4MCBG/V6AQAAAFapKpVKpUoPQcvS2NiYmpqaNDQ0pLq6utLjsAH0GO0OPCiy+ZcMqvQIwAfwMxSKzc/QlsPvoS2fO7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7AAAAAAKDQBCwAAAIBCE7BYo+uuuy49evRIhw4dstdee2XmzJmVHgkAAAD4hBKwWM3tt9+eUaNG5bzzzstTTz2VXXbZJQMHDszChQsrPRoAAADwCSRgsZorrrgiJ510Ur7+9a+nT58+mTBhQjp16pSbbrqp0qMBAAAAn0BtKj0AxbJs2bLMmjUrY8aMKW9r1apVBgwYkBkzZqzxa5YuXZqlS5eW3zc0NCRJGhsbP95h2WhWLn270iMAa+F/b6G4/AyFYvMztOVY9d+yVCpVeBI+LgIWTbz++utZsWJFamtrm2yvra3N3Llz1/g148aNy/e+973Vtnfv3v1jmRGApmquqvQEANA8+Rna8ixZsiQ1NTWVHoOPgYDFehszZkxGjRpVfr9y5cosWrQoW2yxRaqqqio4GbAmjY2N6d69e1555ZVUV1dXehwAaDb8DIXiKpVKWbJkSerq6io9Ch8TAYsmttxyy7Ru3ToLFixosn3BggXp1q3bGr+mffv2ad++fZNtnTt3/rhGBDaQ6upq/+cbAD4CP0OhmNx51bJ5iDtNtGvXLn379s20adPK21auXJlp06alX79+FZwMAAAA+KRyBxarGTVqVE444YTsscce+dznPperrroqb731Vr7+9a9XejQAAADgE0jAYjVf/vKX89prr2Xs2LGpr6/PrrvumilTpqz2YHegeWrfvn3OO++81T76CwCsnZ+hAJVTVfI3JgEAAAAoMM/AAgAAAKDQBCwAAAAACk3AAgAAAKDQBCwAAAAACs1fIQRowV5//fXcdNNNmTFjRurr65Mk3bp1yz777JOvfe1r2WqrrSo8IQAAwD/mrxACtFBPPPFEBg4cmE6dOmXAgAGpra1NkixYsCDTpk3L22+/nfvvvz977LFHhScFAABYOwELoIXae++9s8suu2TChAmpqqpqsq9UKuWb3/xmnnnmmcyYMaNCEwJA8/XKK6/kvPPOy0033VTpUQA+EQQsgBaqY8eO+d3vfpdevXqtcf/cuXOz22675Z133tnIkwFA8/f0009n9913z4oVKyo9CsAngmdgAbRQ3bp1y8yZMz8wYM2cObP8sUIAoKm77757rftfeumljTQJAImABdBinX766Rk2bFhmzZqVgw8+eLVnYP3whz/MZZddVuEpAaCYjjzyyFRVVWVtH1j5+4/oA/Dx8RFCgBbs9ttvz5VXXplZs2aVP+LQunXr9O3bN6NGjcqXvvSlCk8IAMW0zTbb5Prrr8+//uu/rnH/7Nmz07dvXx8hBNhIBCyAT4Dly5fn9ddfT5JsueWWadu2bYUnAoBi++IXv5hdd901F1xwwRr3P/3009ltt92ycuXKjTwZwCeTjxACfAK0bds2W2+9daXHAIBm44wzzshbb731gfu33377PPjggxtxIoBPNndgAQAAAFBorSo9AAAAAACsjYAFAAAAQKEJWAAAAAAUmoAFAPAxmj9/fqqqqjJ79uxKjwIA0GwJWAAAAAAUmoAFANAMLVu2rNIjAABsNAIWAMAGsHLlyowfPz7bb7992rdvn09/+tO5+OKLy/tfeuml9O/fP506dcouu+ySGTNmlPedf/752XXXXZsc76qrrkqPHj3K77/2ta/lyCOPzMUXX5y6urrssMMO5Y8n3nHHHR94bACAlkDAAgDYAMaMGZNLLrkk5557bn7/+99n0qRJqa2tLe8/++yzc/rpp2f27Nn553/+53zlK1/Je++9t07nmDZtWl544YVMnTo1kydP3qDHBgAosjaVHgAAoLlbsmRJrr766lx77bU54YQTkiTbbbdd9t1338yfPz9Jcvrpp2fQoEFJku9973vZcccd84c//CG9evX60OfZZJNN8qMf/Sjt2rVLkg16bACAInMHFgDAenr++eezdOnSHHzwwR+45rOf/Wz531tvvXWSZOHChet0np133rkcrzb0sQEAikzAAgBYTx07dvyHa9q2bVv+d1VVVZK/PTcrSVq1apVSqdRk/fLly1c7xiabbLLOxwYAaAkELACA9fSZz3wmHTt2zLRp0z7S12+11Vapr69vErFmz569gaYDAGj+PAMLAGA9dejQIWeddVbOPPPMtGvXLp///Ofz2muvZc6cOWv9WOEqBx54YF577bWMHz8+xxxzTKZMmZL77rsv1dXVG2F6AIDicwcWAMAGcO655+a0007L2LFj07t373z5y1/+0M+h6t27d66//vpcd9112WWXXTJz5sycfvrpH/PEAADNR1Xp7x+4AAAAAAAF4g4sAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0AQsAAAAAApNwAIAAACg0P4/q1v3y/98RysAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "##we run the code below to create a barchart for churn variable\n",
        "plot_barchart(data, 'churn')\n",
        "%matplot plt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ad4c812",
      "metadata": {
        "id": "1ad4c812"
      },
      "source": [
        "The barchart above shows a larger proportion of customers churned than did not"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d958569e",
      "metadata": {
        "id": "d958569e"
      },
      "source": [
        "## Outliers\n",
        "Next, we check and solve for outliers below following the process below"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dee654c5",
      "metadata": {
        "scrolled": false,
        "colab": {
          "referenced_widgets": [
            "20d279848ec14aacb8e937a5af610678",
            ""
          ]
        },
        "id": "dee654c5",
        "outputId": "11045896-8b3f-4b7e-a0db-f074f555d1ab"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "20d279848ec14aacb8e937a5af610678",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/mnt1/yarn/usercache/livy/appcache/application_1690843046081_0006/container_1690843046081_0006_01_000001/pyspark.zip/pyspark/pandas/utils.py:975: PandasAPIOnSparkAdviceWarning: If `index_col` is not specified for `to_spark`, the existing index is lost when converting to Spark DataFrame.\n",
            "  warnings.warn(message, PandasAPIOnSparkAdviceWarning)"
          ]
        }
      ],
      "source": [
        "#we run the code below to intitialize a spark dataframe\n",
        "data = data.to_spark()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7ba79d1f",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "a0fc85102b8e4fb9b3bd7833dbf6788f",
            ""
          ]
        },
        "id": "7ba79d1f",
        "outputId": "99faf963-b240-4410-d831-bcb04eab7b51"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a0fc85102b8e4fb9b3bd7833dbf6788f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from pyspark.sql import SparkSession"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cfdcb255",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "8f38af8eac2c42988d779969d2a83cec",
            ""
          ]
        },
        "id": "cfdcb255",
        "outputId": "538b47dd-c167-433c-80de-3e13f7829a6a"
      },
      "outputs": [
        {
          "data": {
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              "model_id": "8f38af8eac2c42988d779969d2a83cec",
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              "VBox()"
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          },
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        },
        {
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "spark = SparkSession.builder.getOrCreate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b8ee95db",
      "metadata": {
        "colab": {
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            "a481667217914e7b97e52d222295e8cf",
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      "outputs": [
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        {
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "from pyspark.sql.functions import col, stddev, avg, abs\n",
        "\n",
        "def identify_outliers(dataset, columns, threshold):\n",
        "    spark = SparkSession.builder.getOrCreate()\n",
        "    outliers = {}\n",
        "\n",
        "    for column in columns:\n",
        "        # Compute the mean and standard deviation for the column\n",
        "        mean_stddev = dataset.select(avg(col(column)).alias('mean'), stddev(col(column)).alias('stddev')).first()\n",
        "\n",
        "        # Calculate the z-scores for the column\n",
        "        dataset = dataset.withColumn(column + \"_zscore\", (col(column) - mean_stddev['mean']) / mean_stddev['stddev'])\n",
        "\n",
        "        # Identify outliers based on the threshold\n",
        "        column_outliers = dataset.filter(abs(col(column + \"_zscore\")) > threshold)\n",
        "\n",
        "        # Store outliers in the dictionary\n",
        "        outliers[column] = column_outliers\n",
        "\n",
        "        # Print the column name and its corresponding outliers\n",
        "        print(\"Outliers for column '{}':\".format(column))\n",
        "        column_outliers.show()\n",
        "\n",
        "    return outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "77b10ea9",
      "metadata": {
        "scrolled": true,
        "colab": {
          "referenced_widgets": [
            "ade37bf79be943caa92d913732b83f6c",
            ""
          ]
        },
        "id": "77b10ea9",
        "outputId": "f39ac3ff-e517-4bb8-bf7e-b6210e6fc350"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ade37bf79be943caa92d913732b83f6c",
              "version_major": 2,
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              "VBox()"
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          },
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          "output_type": "display_data"
        },
        {
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "# Assuming sparkSession and 'data' is PySpark DataFrame\n",
        "columns = ['age', 'num_dependents', 'estimated_salary', 'calls_made', 'sms_sent', 'data_used']\n",
        "threshold = 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c5109e39",
      "metadata": {
        "scrolled": true,
        "colab": {
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            "fee6212bd81a4cb4a6871e42061c1c71",
            ""
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        "id": "c5109e39",
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          },
          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Outliers for column 'age':\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+\n",
            "|customer_id|telecom_partner|gender|age|state|city|pincode|date_of_registration|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|age_zscore|\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+\n",
            "\n",
            "Outliers for column 'num_dependents':\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+\n",
            "|customer_id|telecom_partner|gender|age|state|city|pincode|date_of_registration|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|age_zscore|num_dependents_zscore|\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+\n",
            "\n",
            "Outliers for column 'estimated_salary':\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+-----------------------+\n",
            "|customer_id|telecom_partner|gender|age|state|city|pincode|date_of_registration|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|age_zscore|num_dependents_zscore|estimated_salary_zscore|\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+-----------------------+\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+-----------------------+\n",
            "\n",
            "Outliers for column 'calls_made':\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+-----------------------+-----------------+\n",
            "|customer_id|telecom_partner|gender|age|state|city|pincode|date_of_registration|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|age_zscore|num_dependents_zscore|estimated_salary_zscore|calls_made_zscore|\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+-----------------------+-----------------+\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+-----------------------+-----------------+\n",
            "\n",
            "Outliers for column 'sms_sent':\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+-----------------------+-----------------+---------------+\n",
            "|customer_id|telecom_partner|gender|age|state|city|pincode|date_of_registration|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|age_zscore|num_dependents_zscore|estimated_salary_zscore|calls_made_zscore|sms_sent_zscore|\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+-----------------------+-----------------+---------------+\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+-----------------------+-----------------+---------------+\n",
            "\n",
            "Outliers for column 'data_used':\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+-----------------------+-----------------+---------------+----------------+\n",
            "|customer_id|telecom_partner|gender|age|state|city|pincode|date_of_registration|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|age_zscore|num_dependents_zscore|estimated_salary_zscore|calls_made_zscore|sms_sent_zscore|data_used_zscore|\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+-----------------------+-----------------+---------------+----------------+\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+----------+---------------------+-----------------------+-----------------+---------------+----------------+"
          ]
        }
      ],
      "source": [
        "# Call the function to identify outliers for the specified columns\n",
        "identified_outliers = identify_outliers(data, columns, threshold)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e5e62eda",
      "metadata": {
        "id": "e5e62eda"
      },
      "source": [
        "\n",
        "After having implemented the z-score threshold for the removal of outliers, the output returns no outliers based based on the threshold used. However, informed by domain knowledge, we believe that negative values do not fit in the context of some of the variables such as \"calls_made\", \"data_used\", \"sms-sent\", hence we undertake their removal in the next steps below:"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9f563ed1",
      "metadata": {
        "id": "9f563ed1"
      },
      "source": [
        "### Negative Values removal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "560d1f0b",
      "metadata": {
        "colab": {
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            "2b921074f9624cfe9634769400f304ab",
            ""
          ]
        },
        "id": "560d1f0b",
        "outputId": "e4fd8516-20ea-4c0f-fce3-a5ccf28ab172"
      },
      "outputs": [
        {
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              "model_id": "2b921074f9624cfe9634769400f304ab",
              "version_major": 2,
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            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "data = data[data.calls_made > 0]\n",
        "data = data[data.data_used > 0]\n",
        "data = data[data.sms_sent>0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4626ed56",
      "metadata": {
        "scrolled": false,
        "colab": {
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            "7f60469367104e61a85601d07372c770",
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          ]
        },
        "id": "4626ed56",
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            ]
          },
          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+-----------+---------------+------+---+--------------+---------+-------+--------------------+--------------+----------------+----------+--------+---------+-----+\n",
            "|customer_id|telecom_partner|gender|age|         state|     city|pincode|date_of_registration|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|\n",
            "+-----------+---------------+------+---+--------------+---------+-------+--------------------+--------------+----------------+----------+--------+---------+-----+\n",
            "|     241859|       Vodafone|     M| 58|       Manipur|Bangalore| 500407| 2023-04-25 00:00:00|             2|           41014|        61|      45|     9961|    1|\n",
            "|      17876|         Airtel|     M| 59|       Mizoram|   Mumbai| 507230| 2020-03-30 00:00:00|             0|           92175|        92|      12|     1140|    0|\n",
            "|     200647|           BSNL|     M| 28|         Bihar|   Mumbai| 384932| 2022-10-01 00:00:00|             1|          149241|        22|      28|     5135|    0|\n",
            "|     129137|         Airtel|     F| 63| Uttar Pradesh|    Delhi| 645616| 2021-10-08 00:00:00|             1|          108624|        85|      38|     7912|    0|\n",
            "|     144470|           BSNL|     M| 41|      Nagaland|  Chennai| 801648| 2021-12-24 00:00:00|             0|           51171|        69|      38|     9430|    0|\n",
            "|     168367|         Airtel|     M| 42|   Maharashtra|    Delhi| 714941| 2022-04-22 00:00:00|             0|          105182|        64|      23|     7491|    0|\n",
            "|       5557|           BSNL|     M| 36|        Sikkim|Hyderabad| 702156| 2020-01-28 00:00:00|             0|           39845|        82|       8|     4081|    1|\n",
            "|     209859|         Airtel|     F| 21|Andhra Pradesh|    Delhi| 408582| 2022-11-16 00:00:00|             3|           71564|        75|      14|     2558|    0|\n",
            "|     170415|   Reliance Jio|     F| 62|       Manipur|Hyderabad| 172311| 2022-05-02 00:00:00|             3|           78899|        32|      39|     3582|    0|\n",
            "|      40860|           BSNL|     M| 31|           Goa|    Delhi| 348866| 2020-07-23 00:00:00|             0|           63155|        15|       7|     3850|    0|\n",
            "+-----------+---------------+------+---+--------------+---------+-------+--------------------+--------------+----------------+----------+--------+---------+-----+\n",
            "only showing top 10 rows"
          ]
        }
      ],
      "source": [
        "#We run the code below to remove observations with data_used less than 500 MB\n",
        "no_outliers = data[data.data_used > 500]\n",
        "no_outliers.show(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2a41e6f5",
      "metadata": {
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            "ad1dbc125fb6411da9a708c0ad1c63d3",
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          ]
        },
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        "outputId": "873e7ed0-2785-4340-fbf7-8538087c8558"
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      "outputs": [
        {
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              "VBox()"
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from pyspark.sql import SparkSession\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2a9fc488",
      "metadata": {
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              "VBox()"
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          },
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        {
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "def plot_hist(data, features, n_rows, n_cols):\n",
        "    fig, ax = plt.subplots(n_rows, n_cols, figsize=(10,10))\n",
        "    fig.tight_layout(pad=5.0)\n",
        "\n",
        "    for i, feature in enumerate(features):\n",
        "        # Collect column data to driver node\n",
        "        column_data = data.select(feature).rdd.flatMap(lambda x: x).collect()\n",
        "\n",
        "        # Plot histogram on the appropriate subplot\n",
        "        ax[i//n_cols, i%n_cols].hist(column_data, bins=30, edgecolor='black')\n",
        "        ax[i//n_cols, i%n_cols].set_title(feature)\n",
        "\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "359e6ae3",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "a4f89e96b06e4c2abd2cb90f5cc9a334",
            ""
          ]
        },
        "id": "359e6ae3",
        "outputId": "0db784d2-e528-4155-bd8f-5095a9586a4f"
      },
      "outputs": [
        {
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              "VBox()"
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "features = ['estimated_salary', 'calls_made', 'data_used', 'sms_sent']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b60c19d",
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        "id": "7b60c19d",
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#we run the code the below to plot the histograms after removing the outlier-\n",
        "plot_hist(data=no_outliers, features=features, n_rows=2, n_cols=2)\n",
        "%matplot plt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c9e48f51",
      "metadata": {
        "id": "c9e48f51"
      },
      "source": [
        "Regardless we observe that the distribution of the histograms is not any significantly impacted, leading us to conclude that the outlying values have no effect on the overall distribution of the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "de352527",
      "metadata": {
        "id": "de352527"
      },
      "source": [
        "# Milestone III Implementation Starts Here"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a671f6b9",
      "metadata": {
        "id": "a671f6b9"
      },
      "source": [
        "### Correlation Analysis\n",
        "\n",
        "We perform correlation analysis on the numerical variables to assess the strength and direction of their linear relationship. We run the code below to implement this"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "638dbf20",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "bc8f3ce950f74e0695ef297e50c185fa",
            ""
          ]
        },
        "id": "638dbf20",
        "outputId": "ee3461ce-d3e8-44f1-d7b0-8327df4f47b1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bc8f3ce950f74e0695ef297e50c185fa",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Correlation between age and num_dependents: 0.00033377694149931426\n",
            "Correlation between age and estimated_salary: -0.019354468901486798\n",
            "Correlation between age and calls_made: -0.007201311915704921\n",
            "Correlation between age and sms_sent: 0.0020869709447342293\n",
            "Correlation between age and data_used: 0.011210998618513884\n",
            "Correlation between num_dependents and estimated_salary: -0.0073430168523218285\n",
            "Correlation between num_dependents and calls_made: -0.003129220140796443\n",
            "Correlation between num_dependents and sms_sent: 0.014736442509158287\n",
            "Correlation between num_dependents and data_used: -0.008198120363605038\n",
            "Correlation between estimated_salary and calls_made: -0.011571930979899123\n",
            "Correlation between estimated_salary and sms_sent: 0.003144196870527887\n",
            "Correlation between estimated_salary and data_used: -0.024425561824776412\n",
            "Correlation between calls_made and sms_sent: 0.00990435339856176\n",
            "Correlation between calls_made and data_used: 0.004751531107597839\n",
            "Correlation between sms_sent and data_used: -0.014335972974326008"
          ]
        }
      ],
      "source": [
        "from pyspark.sql.functions import corr\n",
        "\n",
        "numerical_features = ['age', 'num_dependents', 'estimated_salary', 'calls_made', 'sms_sent', 'data_used']\n",
        "\n",
        "for i in range(0, len(numerical_features)):\n",
        "    for j in range(i+1, len(numerical_features)):\n",
        "        correlation_value = data.stat.corr(numerical_features[i], numerical_features[j])\n",
        "        print(f\"Correlation between {numerical_features[i]} and {numerical_features[j]}: {correlation_value}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1aacd2b5",
      "metadata": {
        "id": "1aacd2b5"
      },
      "source": [
        "Following this general guide to correlation interpretation:\n",
        "- Close to +1: A strong positive relationship\n",
        "- Close to -1: A strong negative relationship\n",
        "- Close to 0: No or a very weak relationship\n",
        "\n",
        "In general, these correlations are very weak positive and negative relationships, suggesting that there is little linear relationship between these pairs of variables. However, there could still be non-linear relationships, or relationships involving more than two variables, which aren't captured by these simple pairwise correlations."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "06928514",
      "metadata": {
        "id": "06928514"
      },
      "source": [
        "**Initializing the next phase**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "918cc038",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "4789fcb4b75b4b62977c4ea243335580",
            ""
          ]
        },
        "id": "918cc038",
        "outputId": "9f9781b6-1513-45a9-c172-a9cca36fcad1"
      },
      "outputs": [
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              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting Spark application\n"
          ]
        },
        {
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              "<table>\n",
              "<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>User</th><th>Current session?</th></tr><tr><td>0</td><td>application_1691077729082_0001</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"http://ip-172-31-69-165.ec2.internal:20888/proxy/application_1691077729082_0001/\" class=\"emr-proxy-link\" emr-resource=\"j-2IVD29HXHPKSH\n",
              "\" application-id=\"application_1691077729082_0001\">Link</a></td><td><a target=\"_blank\" href=\"http://ip-172-31-66-59.ec2.internal:8042/node/containerlogs/container_1691077729082_0001_01_000001/livy\" >Link</a></td><td>None</td><td>✔</td></tr></table>"
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          },
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SparkSession available as 'spark'.\n"
          ]
        },
        {
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+-----------+---------------+------+---+-------------+---------+-------+--------------------+--------------+----------------+----------+--------+---------+-----+\n",
            "|customer_id|telecom_partner|gender|age|        state|     city|pincode|date_of_registration|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|\n",
            "+-----------+---------------+------+---+-------------+---------+-------+--------------------+--------------+----------------+----------+--------+---------+-----+\n",
            "|     241859|       Vodafone|     M| 58|      Manipur|Bangalore| 500407| 2023-04-25 00:00:00|             2|           41014|        61|      45|     9961|    1|\n",
            "|      17876|         Airtel|     M| 59|      Mizoram|   Mumbai| 507230| 2020-03-30 00:00:00|             0|           92175|        92|      12|     1140|    0|\n",
            "|      38273|           BSNL|     M| 40|        Bihar|  Chennai| 453495| 2020-07-10 00:00:00|             2|          109436|        82|      19|     -604|    0|\n",
            "|     200647|           BSNL|     M| 28|        Bihar|   Mumbai| 384932| 2022-10-01 00:00:00|             1|          149241|        22|      28|     5135|    0|\n",
            "|     129137|         Airtel|     F| 63|Uttar Pradesh|    Delhi| 645616| 2021-10-08 00:00:00|             1|          108624|        85|      38|     7912|    0|\n",
            "+-----------+---------------+------+---+-------------+---------+-------+--------------------+--------------+----------------+----------+--------+---------+-----+\n",
            "only showing top 5 rows"
          ]
        }
      ],
      "source": [
        "from pyspark.sql import SparkSession\n",
        "from pyspark.ml.feature import StringIndexer, VectorAssembler, StandardScaler, ChiSqSelector\n",
        "from pyspark.ml import Pipeline\n",
        "from pyspark.ml.classification import LogisticRegression\n",
        "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n",
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
        "\n",
        "# create Spark session\n",
        "spark = SparkSession.builder.appName(\"telecom_churn_prediction\").getOrCreate()\n",
        "\n",
        "# load the data\n",
        "data = spark.read.csv('s3://jiogboles3private/spark/new_telcom_churn.csv', inferSchema=True, header=True)\n",
        "\n",
        "# show the first few records\n",
        "data.show(5)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ded21d2f",
      "metadata": {
        "id": "ded21d2f"
      },
      "source": [
        "As always, it is crucial to check the dataset for missing/null/NA values. we do this below"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "69dcd90b",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "dd7851dcbf514e9fa399048f76036e77",
            ""
          ]
        },
        "id": "69dcd90b",
        "outputId": "f3b2a5bd-5da8-4b89-c3f6-c1fbe7d1c968"
      },
      "outputs": [
        {
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              "model_id": "dd7851dcbf514e9fa399048f76036e77",
              "version_major": 2,
              "version_minor": 0
            },
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              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+\n",
            "|customer_id|telecom_partner|gender|age|state|city|pincode|date_of_registration|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+\n",
            "|          0|              0|     0|  0|    0|   0|      0|                   0|             0|               0|         0|       0|        0|    0|\n",
            "+-----------+---------------+------+---+-----+----+-------+--------------------+--------------+----------------+----------+--------+---------+-----+"
          ]
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      ],
      "source": [
        "#We run the following code to check if there are any empty or null values in the dataset\n",
        "from pyspark.sql.functions import col, sum as sql_sum\n",
        "\n",
        "# Checking for null or NA values\n",
        "data.select(*(sql_sum(col(c).isNull().cast('int')).alias(c) for c in data.columns)).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "52e5b803",
      "metadata": {
        "id": "52e5b803"
      },
      "source": [
        "**Data Transformation**\n",
        "\n",
        "We begin the process below. first we drop columns that are irrelevant to the analysis and modeling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ae0390dc",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "be1484d1acd1487185470a43a438913c",
            ""
          ]
        },
        "id": "ae0390dc",
        "outputId": "3cfe1a7d-35e0-420f-fd5f-e9f2195419ec"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "be1484d1acd1487185470a43a438913c",
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              "VBox()"
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      ],
      "source": [
        "#drop columns not needed\n",
        "data = data.drop('customer_id', 'date_of_registration')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4518d6c8",
      "metadata": {
        "colab": {
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            "1b40894c6ba04981947c882e5f885338",
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          ]
        },
        "id": "4518d6c8",
        "outputId": "ef01a45d-7f78-4f29-afb4-7beb00dcd353"
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      "outputs": [
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              "model_id": "1b40894c6ba04981947c882e5f885338",
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              "VBox()"
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          "name": "stdout",
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          "text": [
            "+---------------+------+---+-------+---------+-------+--------------+----------------+----------+--------+---------+-----+\n",
            "|telecom_partner|gender|age|  state|     city|pincode|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|\n",
            "+---------------+------+---+-------+---------+-------+--------------+----------------+----------+--------+---------+-----+\n",
            "|       Vodafone|     M| 58|Manipur|Bangalore| 500407|             2|           41014|        61|      45|     9961|    1|\n",
            "|         Airtel|     M| 59|Mizoram|   Mumbai| 507230|             0|           92175|        92|      12|     1140|    0|\n",
            "+---------------+------+---+-------+---------+-------+--------------+----------------+----------+--------+---------+-----+\n",
            "only showing top 2 rows"
          ]
        }
      ],
      "source": [
        "#print dataframe to confirm dropped columns\n",
        "data.show(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ed318daf",
      "metadata": {
        "id": "ed318daf"
      },
      "source": [
        "**Data Partitioning**\n",
        "\n",
        "Next we split the dataset into test and train sets for the purpose of model evaluation, overfitting detection, hyperparameter tuning and prevention of data leakage."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dc4ae4bb",
      "metadata": {
        "colab": {
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            "b088e1375a3b4a2281878d302c5d7a86",
            ""
          ]
        },
        "id": "dc4ae4bb",
        "outputId": "51cf144d-61c4-4568-faec-e602ca2f8ef8"
      },
      "outputs": [
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          "data": {
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              "model_id": "b088e1375a3b4a2281878d302c5d7a86",
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              "VBox()"
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      ],
      "source": [
        "#split data into train and test sets\n",
        "train_data, test_data = data.randomSplit([0.7, 0.3], seed=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "08871e3c",
      "metadata": {
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      "source": [
        "In the code above, randomSplit function is used to randomly split the data into training and test sets. The argument [0.7, 0.3] means that approximately 70% of the data will be used for model training and 30% as our test dataset"
      ]
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      "outputs": [
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          "text": [
            "9198"
          ]
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      ],
      "source": [
        "#get a count of train set\n",
        "train_data.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "55f9939c",
      "metadata": {
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      "outputs": [
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          "text": [
            "3802"
          ]
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      ],
      "source": [
        "#get a count of test set\n",
        "test_data.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "metadata": {
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        "id": "6e7c3ff2",
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          "text": [
            "+---------------+------+---+-----------------+---------+-------+--------------+----------------+----------+--------+---------+-----+\n",
            "|telecom_partner|gender|age|            state|     city|pincode|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|\n",
            "+---------------+------+---+-----------------+---------+-------+--------------+----------------+----------+--------+---------+-----+\n",
            "|         Airtel|     F| 18|Arunachal Pradesh|Hyderabad| 663657|             3|          140043|         8|      -2|     5154|    0|\n",
            "|         Airtel|     F| 18|Arunachal Pradesh|  Kolkata| 403882|             0|           87551|        67|      22|     3919|    1|\n",
            "+---------------+------+---+-----------------+---------+-------+--------------+----------------+----------+--------+---------+-----+\n",
            "only showing top 2 rows"
          ]
        }
      ],
      "source": [
        "#print train dataframe\n",
        "train_data.show(2)"
      ]
    },
    {
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          "text": [
            "+---------------+------+---+---------+-------+-------+--------------+----------------+----------+--------+---------+-----+\n",
            "|telecom_partner|gender|age|    state|   city|pincode|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|\n",
            "+---------------+------+---+---------+-------+-------+--------------+----------------+----------+--------+---------+-----+\n",
            "|         Airtel|     F| 18|    Assam|Chennai| 786664|             4|          122587|        71|      36|     7230|    0|\n",
            "|         Airtel|     F| 18|Rajasthan| Mumbai| 545310|             3|          145860|        99|      16|     5723|    1|\n",
            "+---------------+------+---+---------+-------+-------+--------------+----------------+----------+--------+---------+-----+\n",
            "only showing top 2 rows"
          ]
        }
      ],
      "source": [
        "#print test dataframe\n",
        "test_data.show(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "51da9d70",
      "metadata": {
        "id": "51da9d70"
      },
      "source": [
        "**Normalization and Dummy Variables Creation ~ Modeling Pipeline Construction**\n",
        "\n",
        "Below we normalize the numerical variables to adjust our input features to a common scale without distorting the differences in range of values or losing information\n",
        "\n",
        "Normalizing numerical features, such as 'estimated_salary', 'calls_made', 'sms_sent', and 'data_used', is essential as it brings all the values into a similar scale\n",
        "\n",
        "For categorical variables like 'gender', 'state', 'city', etc. we create n-1 dummy variables to avoid the dummy variables trap.\n",
        "\n",
        "In the code below, StringIndexer transforms each categorical column to a column of numerical indices, and OneHotEncoder maps a column of category indices to a column of binary vectors.\n",
        "\n",
        "In PySpark's ML library, many functions (including the StandardScaler) require that the input data is assembled into a single column of vectors, not a DataFrame of scalar columns, we implement all of these in the cells that follow."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fa8e602a",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "dca9a268d58949118784a0eb62a3b2d3",
            ""
          ]
        },
        "id": "fa8e602a",
        "outputId": "5d81dd3d-f753-4570-a9a2-619d1917794d"
      },
      "outputs": [
        {
          "data": {
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              "model_id": "dca9a268d58949118784a0eb62a3b2d3",
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            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+------+------------+\n",
            "|gender|gender_index|\n",
            "+------+------------+\n",
            "|     M|         0.0|\n",
            "|     M|         0.0|\n",
            "|     M|         0.0|\n",
            "|     M|         0.0|\n",
            "|     F|         1.0|\n",
            "|     M|         0.0|\n",
            "|     M|         0.0|\n",
            "|     M|         0.0|\n",
            "|     F|         1.0|\n",
            "|     F|         1.0|\n",
            "|     F|         1.0|\n",
            "|     M|         0.0|\n",
            "|     M|         0.0|\n",
            "|     M|         0.0|\n",
            "|     M|         0.0|\n",
            "|     M|         0.0|\n",
            "|     M|         0.0|\n",
            "|     M|         0.0|\n",
            "|     M|         0.0|\n",
            "|     F|         1.0|\n",
            "+------+------------+\n",
            "only showing top 20 rows"
          ]
        }
      ],
      "source": [
        "# Apply StringIndexer to 'gender' column and display the result\n",
        "indexer = StringIndexer(inputCol=\"gender\", outputCol=\"gender_index\")\n",
        "indexed = indexer.fit(data).transform(data)\n",
        "indexed.select(\"gender\", \"gender_index\").show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ce03e13",
      "metadata": {
        "id": "1ce03e13"
      },
      "source": [
        "In the cell output above, we applied the stringindexer transformation to the gender column as test to ensure that our categorical tranformation is being correctly applied. The output is as expected. We also do the same for \"state\" and \"city\" categorical variables."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bbe2e06e",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "b7b3de56f5d44ca3a67b3d6d94036fb7",
            ""
          ]
        },
        "id": "bbe2e06e",
        "outputId": "f8121dae-299b-472d-ca52-21f65514e16c"
      },
      "outputs": [
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              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
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          },
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        },
        {
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          },
          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+--------------+-----------+\n",
            "|         state|state_index|\n",
            "+--------------+-----------+\n",
            "|       Manipur|       19.0|\n",
            "|       Mizoram|        8.0|\n",
            "|         Bihar|       14.0|\n",
            "|         Bihar|       14.0|\n",
            "| Uttar Pradesh|       11.0|\n",
            "|      Nagaland|       17.0|\n",
            "|   Maharashtra|        6.0|\n",
            "|        Sikkim|        1.0|\n",
            "|Andhra Pradesh|       21.0|\n",
            "|       Manipur|       19.0|\n",
            "|     Telangana|        7.0|\n",
            "|           Goa|       12.0|\n",
            "|    Tamil Nadu|       24.0|\n",
            "|Andhra Pradesh|       21.0|\n",
            "|Madhya Pradesh|        9.0|\n",
            "|       Manipur|       19.0|\n",
            "|        Sikkim|        1.0|\n",
            "|       Mizoram|        8.0|\n",
            "|     Telangana|        7.0|\n",
            "|       Mizoram|        8.0|\n",
            "+--------------+-----------+\n",
            "only showing top 20 rows\n",
            "\n",
            "+---------+----------+\n",
            "|     city|city_index|\n",
            "+---------+----------+\n",
            "|Bangalore|       1.0|\n",
            "|   Mumbai|       2.0|\n",
            "|  Chennai|       4.0|\n",
            "|   Mumbai|       2.0|\n",
            "|    Delhi|       3.0|\n",
            "|  Chennai|       4.0|\n",
            "|    Delhi|       3.0|\n",
            "|Hyderabad|       0.0|\n",
            "|    Delhi|       3.0|\n",
            "|Hyderabad|       0.0|\n",
            "|Hyderabad|       0.0|\n",
            "|    Delhi|       3.0|\n",
            "|Bangalore|       1.0|\n",
            "|Bangalore|       1.0|\n",
            "|    Delhi|       3.0|\n",
            "|   Mumbai|       2.0|\n",
            "|    Delhi|       3.0|\n",
            "|  Chennai|       4.0|\n",
            "|Bangalore|       1.0|\n",
            "|  Kolkata|       5.0|\n",
            "+---------+----------+\n",
            "only showing top 20 rows"
          ]
        }
      ],
      "source": [
        "# Apply StringIndexer to 'state' column and display the result\n",
        "indexer = StringIndexer(inputCol=\"state\", outputCol=\"state_index\")\n",
        "indexed = indexer.fit(data).transform(data)\n",
        "indexed.select(\"state\", \"state_index\").show()\n",
        "\n",
        "# Apply StringIndexer to 'city' column and display the result\n",
        "indexer = StringIndexer(inputCol=\"city\", outputCol=\"city_index\")\n",
        "indexed = indexer.fit(data).transform(data)\n",
        "indexed.select(\"city\", \"city_index\").show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f8fd0f36",
      "metadata": {
        "id": "f8fd0f36"
      },
      "source": [
        "The output above is as expected and confirms the stringindexers transformation is implementing as specified. Let's continue."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8bda1406",
      "metadata": {
        "id": "8bda1406"
      },
      "source": [
        "**Below** we implement the categorical transformation(dummy creation) on the dataframe as a whole"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cd6c26f6",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "66b30bac28834f39b90aa343ee1445db",
            ""
          ]
        },
        "id": "cd6c26f6",
        "outputId": "20021b1b-dbe2-4665-da7b-49b2ca4f234b"
      },
      "outputs": [
        {
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              "model_id": "66b30bac28834f39b90aa343ee1445db",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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          },
          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+---------------+------+---+--------------+---------+-------+--------------+----------------+----------+--------+---------+-----+-------------+------------+----------+-----------+---------------------+\n",
            "|telecom_partner|gender|age|         state|     city|pincode|num_dependents|estimated_salary|calls_made|sms_sent|data_used|churn|pincode_index|gender_index|city_index|state_index|telecom_partner_index|\n",
            "+---------------+------+---+--------------+---------+-------+--------------+----------------+----------+--------+---------+-----+-------------+------------+----------+-----------+---------------------+\n",
            "|       Vodafone|     M| 58|       Manipur|Bangalore| 500407|             2|           41014|        61|      45|     9961|    1|       5891.0|         0.0|       1.0|       19.0|                  3.0|\n",
            "|         Airtel|     M| 59|       Mizoram|   Mumbai| 507230|             0|           92175|        92|      12|     1140|    0|       6001.0|         0.0|       2.0|        8.0|                  2.0|\n",
            "|           BSNL|     M| 40|         Bihar|  Chennai| 453495|             2|          109436|        82|      19|     -604|    0|       5214.0|         0.0|       4.0|       14.0|                  0.0|\n",
            "|           BSNL|     M| 28|         Bihar|   Mumbai| 384932|             1|          149241|        22|      28|     5135|    0|       4215.0|         0.0|       2.0|       14.0|                  0.0|\n",
            "|         Airtel|     F| 63| Uttar Pradesh|    Delhi| 645616|             1|          108624|        85|      38|     7912|    0|       7957.0|         1.0|       3.0|       11.0|                  2.0|\n",
            "|           BSNL|     M| 41|      Nagaland|  Chennai| 801648|             0|           51171|        69|      38|     9430|    0|      10172.0|         0.0|       4.0|       17.0|                  0.0|\n",
            "|         Airtel|     M| 42|   Maharashtra|    Delhi| 714941|             0|          105182|        64|      23|     7491|    0|       8930.0|         0.0|       3.0|        6.0|                  2.0|\n",
            "|           BSNL|     M| 36|        Sikkim|Hyderabad| 702156|             0|           39845|        82|       8|     4081|    1|       8743.0|         0.0|       0.0|        1.0|                  0.0|\n",
            "|         Airtel|     F| 21|Andhra Pradesh|    Delhi| 408582|             3|           71564|        75|      14|     2558|    0|       4565.0|         1.0|       3.0|       21.0|                  2.0|\n",
            "|   Reliance Jio|     F| 62|       Manipur|Hyderabad| 172311|             3|           78899|        32|      39|     3582|    0|       1179.0|         1.0|       0.0|       19.0|                  1.0|\n",
            "|   Reliance Jio|     F| 52|     Telangana|Hyderabad| 154773|             4|           66587|        86|      21|      383|    0|        926.0|         1.0|       0.0|        7.0|                  1.0|\n",
            "|           BSNL|     M| 31|           Goa|    Delhi| 348866|             0|           63155|        15|       7|     3850|    0|       3711.0|         0.0|       3.0|       12.0|                  0.0|\n",
            "|         Airtel|     M| 64|    Tamil Nadu|Bangalore| 966084|             1|          138603|        27|      44|     8267|    1|      12410.0|         0.0|       1.0|       24.0|                  2.0|\n",
            "|       Vodafone|     M| 70|Andhra Pradesh|Bangalore| 285811|             1|           30752|        32|      44|       27|    0|       2831.0|         0.0|       1.0|       21.0|                  3.0|\n",
            "|         Airtel|     M| 38|Madhya Pradesh|    Delhi| 236139|             1|           79524|       101|      32|     4860|    0|       2123.0|         0.0|       3.0|        9.0|                  2.0|\n",
            "|         Airtel|     M| 66|       Manipur|   Mumbai| 971149|             0|           52357|        35|       9|     4100|    0|      12473.0|         0.0|       2.0|       19.0|                  2.0|\n",
            "|   Reliance Jio|     M| 35|        Sikkim|    Delhi| 402487|             2|          108597|        69|      16|     6751|    0|       4481.0|         0.0|       3.0|        1.0|                  1.0|\n",
            "|         Airtel|     M| 61|       Mizoram|  Chennai| 431202|             0|           87146|       102|      29|     1115|    0|       4890.0|         0.0|       4.0|        8.0|                  2.0|\n",
            "|           BSNL|     M| 22|     Telangana|Bangalore| 267724|             0|           28267|        22|      47|      541|    1|       2578.0|         0.0|       1.0|        7.0|                  0.0|\n",
            "|         Airtel|     F| 64|       Mizoram|  Kolkata| 283673|             3|           73611|        89|      11|     1156|    0|       2795.0|         1.0|       5.0|        8.0|                  2.0|\n",
            "+---------------+------+---+--------------+---------+-------+--------------+----------------+----------+--------+---------+-----+-------------+------------+----------+-----------+---------------------+\n",
            "only showing top 20 rows"
          ]
        }
      ],
      "source": [
        "# List of numerical columns\n",
        "numerical_columns = ['age', 'num_dependents', 'estimated_salary', 'calls_made', 'sms_sent', 'data_used']\n",
        "\n",
        "# List of categorical columns\n",
        "categorical_columns = list(set(data.columns) - set(numerical_columns) - set(['churn']))\n",
        "\n",
        "# StringIndexer for categorical columns (n-1 dummies)\n",
        "indexers = [StringIndexer(inputCol=column, outputCol=column+\"_index\").setHandleInvalid(\"keep\")\n",
        "            for column in categorical_columns]\n",
        "\n",
        "# Apply StringIndexer to all categorical columns\n",
        "pipeline = Pipeline(stages=indexers)\n",
        "data_indexed = pipeline.fit(data).transform(data)\n",
        "\n",
        "# Display the DataFrame after StringIndexer\n",
        "data_indexed.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "641b8405",
      "metadata": {
        "id": "641b8405"
      },
      "source": [
        "The output is as specified and expected.Let's continue"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "455bc9ee",
      "metadata": {
        "id": "455bc9ee"
      },
      "source": [
        "**Next** we turn to the transformation(normalization) of the numerical variables as described in following order:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "52eda525",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "e9bc054102ba478e8728d60c205d01a6",
            ""
          ]
        },
        "id": "52eda525",
        "outputId": "4bd4636e-a959-47a1-85c6-82c7011e1a8c"
      },
      "outputs": [
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+--------------------+\n",
            "|            features|\n",
            "+--------------------+\n",
            "|[5891.0,0.0,1.0,1...|\n",
            "|[6001.0,0.0,2.0,8...|\n",
            "|[5214.0,0.0,4.0,1...|\n",
            "|[4215.0,0.0,2.0,1...|\n",
            "|[7957.0,1.0,3.0,1...|\n",
            "|[10172.0,0.0,4.0,...|\n",
            "|[8930.0,0.0,3.0,6...|\n",
            "|[8743.0,0.0,0.0,1...|\n",
            "|[4565.0,1.0,3.0,2...|\n",
            "|[1179.0,1.0,0.0,1...|\n",
            "+--------------------+\n",
            "only showing top 10 rows"
          ]
        }
      ],
      "source": [
        "# VectorAssembler for numerical columns\n",
        "assemblerInputs = [c + \"_index\" for c in categorical_columns] + numerical_columns\n",
        "assembler = VectorAssembler(inputCols=assemblerInputs, outputCol=\"features\")\n",
        "\n",
        "# Transform data using VectorAssembler\n",
        "assembled_data = assembler.transform(data_indexed)\n",
        "\n",
        "# Display the DataFrame after VectorAssembler\n",
        "assembled_data.select(\"features\").show(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "70b8d01f",
      "metadata": {
        "id": "70b8d01f"
      },
      "source": [
        "**The** output above confirms that the numerical transformation is correctly applied. We'll go ahead and normalize all assembled numerical vectors below"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b781a119",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "bf15640100734ffc9fc7897c95539b54",
            ""
          ]
        },
        "id": "b781a119",
        "outputId": "2e870dc7-1419-44e6-9360-acc888a3a7d7"
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              "VBox()"
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        {
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          "output_type": "stream",
          "text": [
            "+--------------------+\n",
            "|      scaledFeatures|\n",
            "+--------------------+\n",
            "|[1.57032696625958...|\n",
            "|[1.59964897717259...|\n",
            "|[1.38986331727677...|\n",
            "|[1.12356614543950...|\n",
            "|[2.12104764395306...|\n",
            "|[2.71148631824690...|\n",
            "|[2.38041415866543...|\n",
            "|[2.33056674011331...|\n",
            "|[1.21686345289000...|\n",
            "|[0.31427864424037...|\n",
            "+--------------------+\n",
            "only showing top 10 rows"
          ]
        }
      ],
      "source": [
        "# StandardScaler for normalization\n",
        "scaler = StandardScaler(inputCol=\"features\", outputCol=\"scaledFeatures\", withStd=True, withMean=False)\n",
        "\n",
        "# Transform data using StandardScaler\n",
        "scaled_data = scaler.fit(assembled_data).transform(assembled_data)\n",
        "\n",
        "# Display the DataFrame after StandardScaler\n",
        "scaled_data.select(\"scaledFeatures\").show(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3c207898",
      "metadata": {
        "id": "3c207898"
      },
      "source": [
        "The output above is as expected, let's continue"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "da54b6a2",
      "metadata": {
        "id": "da54b6a2"
      },
      "source": [
        "**Next** we implement the **logistic regression** **modeling, hyperparameter tuning** and make **predictions** on our test dataset"
      ]
    },
    {
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      "id": "31474ebe",
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              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Logistic Regression model\n",
        "lr = LogisticRegression(featuresCol=\"scaledFeatures\", labelCol=\"churn\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af6da3c3",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "2e00776066a644e48de0dde682c4dcb6",
            ""
          ]
        },
        "id": "af6da3c3",
        "outputId": "7fbd39c0-1f72-4dcb-e965-e256918c94f2"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2e00776066a644e48de0dde682c4dcb6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-16:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Hyperparameter tuning\n",
        "# Define the parameter grid\n",
        "paramGrid = ParamGridBuilder() \\\n",
        "    .addGrid(lr.regParam, [0.01, 0.5, 2.0]) \\\n",
        "    .addGrid(lr.elasticNetParam, [0.0, 0.5, 1.0]) \\\n",
        "    .build()\n",
        "\n",
        "# Define the cross-validation\n",
        "crossval = CrossValidator(estimator=lr,\n",
        "                          estimatorParamMaps=paramGrid,\n",
        "                          evaluator=BinaryClassificationEvaluator(labelCol='churn'),\n",
        "                          numFolds=3)\n",
        "\n",
        "# Run cross-validation, and choose the best set of parameters.\n",
        "cvModel = crossval.fit(scaled_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "86cbce2b",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "3e3f1872a91840caaa5f6cb9708552fa",
            ""
          ]
        },
        "id": "86cbce2b",
        "outputId": "abbf2ea6-eebb-4c1c-c19c-eb6f7f3f058d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3e3f1872a91840caaa5f6cb9708552fa",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-17:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Make predictions on the test data\n",
        "predictions = cvModel.transform(scaled_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6616ef43",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "f3cba53a36b34718bfcb355b609d482c",
            ""
          ]
        },
        "id": "6616ef43",
        "outputId": "2e31ef28-657f-4370-e58d-a65e4a9916d7"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f3cba53a36b34718bfcb355b609d482c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-18:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+----------+-----+\n",
            "|prediction|churn|\n",
            "+----------+-----+\n",
            "|       0.0|    1|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    1|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    1|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    1|\n",
            "|       0.0|    0|\n",
            "+----------+-----+\n",
            "only showing top 20 rows"
          ]
        }
      ],
      "source": [
        "# Show predictions\n",
        "predictions.select(\"prediction\", \"churn\").show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "042de2a0",
      "metadata": {
        "id": "042de2a0"
      },
      "source": [
        "**The output** above is as anticipated. To describe what has transpired above in the three cells above: This code performs hyperparameter tuning on the logistic regression model, using a grid of regParam and elasticNetParam values. It uses 3-fold cross-validation, and selects the model with the best AUC-ROC as measured by the BinaryClassificationEvaluator. The best model is then used to make predictions on the test data."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "450640c9",
      "metadata": {
        "id": "450640c9"
      },
      "source": [
        "**Having applied**  the logistic regression model to make predictions on our test data, we proceed to calculate the model performance metrics as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "027ea581",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "7e8f42029dd646e6b2872a41b05c6cbd",
            ""
          ]
        },
        "id": "027ea581",
        "outputId": "37fda2a3-ba9b-40cd-a8f4-64e9b575ce4f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e8f42029dd646e6b2872a41b05c6cbd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-21:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Performance Summary:\n",
            "Accuracy = 0.80\n",
            "Precision = 0.64\n",
            "Recall = 0.80\n",
            "F1 Score = 0.71\n",
            "AUC = 0.50"
          ]
        }
      ],
      "source": [
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator, MulticlassClassificationEvaluator\n",
        "\n",
        "# Define the evaluators\n",
        "binary_evaluator = BinaryClassificationEvaluator(labelCol='churn')\n",
        "multi_evaluator = MulticlassClassificationEvaluator(labelCol='churn')\n",
        "\n",
        "# Compute the evaluation metrics\n",
        "accuracy = multi_evaluator.evaluate(predictions, {multi_evaluator.metricName: \"accuracy\"})\n",
        "precision = multi_evaluator.evaluate(predictions, {multi_evaluator.metricName: \"weightedPrecision\"})\n",
        "recall = multi_evaluator.evaluate(predictions, {multi_evaluator.metricName: \"weightedRecall\"})\n",
        "f1 = multi_evaluator.evaluate(predictions, {multi_evaluator.metricName: \"f1\"})\n",
        "auc = binary_evaluator.evaluate(predictions)\n",
        "\n",
        "# Print the evaluation metrics\n",
        "print(\"Model Performance Summary:\")\n",
        "print(\"Accuracy = {:.2f}\".format(accuracy))\n",
        "print(\"Precision = {:.2f}\".format(precision))\n",
        "print(\"Recall = {:.2f}\".format(recall))\n",
        "print(\"F1 Score = {:.2f}\".format(f1))\n",
        "print(\"AUC = {:.2f}\".format(auc))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "68438925",
      "metadata": {
        "id": "68438925"
      },
      "source": [
        "**We interpret these numbers below**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b23621c1",
      "metadata": {
        "id": "b23621c1"
      },
      "source": [
        "- Accuracy (0.8): This means that 80% of the time, the model correctly predicted whether a customer churned or not.\n",
        "- Precision (0.64): Precision is the proportion of true positive predictions (in this case, customers that churned and were correctly predicted as such) out of all positive predictions (all customers predicted to churn). So, of all the customers that the model predicted would churn, 64% of them actually did churn.\n",
        "- Recall (0.8): Recall, or sensitivity, is the proportion of true positive predictions out of all actual positives (all customers who actually churned). So,the model correctly identified 80% of the customers who churned.\n",
        "- F1 Score (0.71): The F1 Score is the harmonic mean of precision and recall. It's a single metric that combines both precision and recall. An F1 Score closer to 1 indicates a better balance between precision and recall. The F1 score is 0.71, which indicates a reasonably good balance.\n",
        "- AUC (0.5): The Area Under the ROC Curve (AUC-ROC) is a measure of the model's ability to distinguish between positive and negative classes. An AUC of 0.5 indicates that the model has no discrimination capacity to distinguish between positive and negative classes.\n",
        "\n",
        "\n",
        "It seems like the model has good accuracy, recall, and a reasonable F1 score, but the AUC is quite low. The low AUC might be a result of the class imbalance in the data, poor predictive power of the features, or it might need further tuning of hyperparameters or more advanced feature engineering.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f4b34817",
      "metadata": {
        "id": "f4b34817"
      },
      "source": [
        "**Below we attempt to print the intercepts and coefficients of the models' predictor variables**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fe745088",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "dae5309a05b5417684ee79a072e63f9f",
            ""
          ]
        },
        "id": "fe745088",
        "outputId": "afc7a9ef-cee5-4f38-948e-c19d268dec69"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dae5309a05b5417684ee79a072e63f9f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Coefficients: (11,[],[])\n",
            "Intercept: -1.3862943611198906"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-22:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Get the best Logistic Regression model\n",
        "bestModel = cvModel.bestModel\n",
        "\n",
        "# Print the coefficients and intercept for logistic regression\n",
        "print(\"Coefficients: \" + str(bestModel.coefficients))\n",
        "print(\"Intercept: \" + str(bestModel.intercept))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d0b39e03",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "38683ee1e4b84ff9ab762f04519dd30c",
            ""
          ]
        },
        "id": "d0b39e03",
        "outputId": "f185de50-66e5-4c65-c898-30e9626a0365"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "38683ee1e4b84ff9ab762f04519dd30c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-23:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Coefficients: (11,[],[])\n",
            "Intercept: -1.3862943611198906\n",
            "ROC: Area Under Curve (AUC) = 0.5\n",
            "('pincode_index', 0.0)\n",
            "('gender_index', 0.0)\n",
            "('city_index', 0.0)\n",
            "('state_index', 0.0)\n",
            "('telecom_partner_index', 0.0)\n",
            "('age', 0.0)\n",
            "('num_dependents', 0.0)\n",
            "('estimated_salary', 0.0)\n",
            "('calls_made', 0.0)\n",
            "('sms_sent', 0.0)\n",
            "('data_used', 0.0)"
          ]
        }
      ],
      "source": [
        "# Get the best Logistic Regression model\n",
        "bestModel = cvModel.bestModel\n",
        "\n",
        "# Print the coefficients and intercept for logistic regression\n",
        "print(\"Coefficients: \" + str(bestModel.coefficients))\n",
        "print(\"Intercept: \" + str(bestModel.intercept))\n",
        "\n",
        "# Obtain the receiver-operating characteristic as a DataFrame and areaUnderROC.\n",
        "trainingSummary = bestModel.summary\n",
        "print(\"ROC: Area Under Curve (AUC) = \" + str(trainingSummary.areaUnderROC))\n",
        "\n",
        "# Get the model coefficients\n",
        "coefficients = bestModel.coefficients\n",
        "\n",
        "# Map feature names to coefficients\n",
        "feature_names = assemblerInputs\n",
        "feature_coefficients = list(zip(feature_names, coefficients))\n",
        "\n",
        "for feature in feature_coefficients:\n",
        "    print(feature)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "429fa799",
      "metadata": {
        "id": "429fa799"
      },
      "source": [
        "**If you observe closely you'll notice something unusual.**\n",
        "\n",
        "The output suggests that all the coefficients for the independent variables in the logistic regression model are 0. This is unusual and implies that, according to the model, none of the features have any influence on the target variable 'churn'.\n",
        "\n",
        "\n",
        "**We believe** this is because the regularization parameter is set too high. When this is the case, it could push all coefficients towards zero."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d5b536ed",
      "metadata": {
        "id": "d5b536ed"
      },
      "source": [
        "**So** we adjust the values of the regularization parameter (regParam) in the model and rerun the model to see if there is any difference in outcome."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c617435d",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "8f589e9eece146a4bc17f7ff1949a03d",
            ""
          ]
        },
        "id": "c617435d",
        "outputId": "298407d9-35e8-4fac-f888-eead2f6fdb2a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8f589e9eece146a4bc17f7ff1949a03d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-25:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Logistic Regression model\n",
        "lr = LogisticRegression(featuresCol=\"scaledFeatures\", labelCol=\"churn\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "58dc6557",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "b0ae4066ea1b4fa29574d4316cc343d7",
            ""
          ]
        },
        "id": "58dc6557",
        "outputId": "101f2b9a-7dda-424d-afdc-d3e9e7f4d9bf"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b0ae4066ea1b4fa29574d4316cc343d7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-26:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Define the parameter grid\n",
        "# Tune hyperparameter. notice the parameter as much lower than before\n",
        "paramGrid = ParamGridBuilder() \\\n",
        "    .addGrid(lr.regParam, [0.0, 0.01, 0.1, 1.0, 10.0]) \\\n",
        "    .addGrid(lr.elasticNetParam, [0.0]) \\\n",
        "    .build()\n",
        "\n",
        "# Define the cross-validation\n",
        "crossval = CrossValidator(estimator=lr,\n",
        "                          estimatorParamMaps=paramGrid,\n",
        "                          evaluator=BinaryClassificationEvaluator(labelCol='churn'),\n",
        "                          numFolds=3)\n",
        "\n",
        "# Run cross-validation, and choose the best set of parameters.\n",
        "cvModel = crossval.fit(scaled_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5f9acb8",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "47ee74af486e4a02a6bcbb2633f51f6d",
            ""
          ]
        },
        "id": "e5f9acb8",
        "outputId": "a37ce693-c1a5-4980-db86-0324d9626e71"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "47ee74af486e4a02a6bcbb2633f51f6d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-27:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Make predictions on the test data\n",
        "predictions = cvModel.transform(scaled_data)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d11ecc23",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "c5feaed3313a4f7a9246dfedf5653364",
            ""
          ]
        },
        "id": "d11ecc23",
        "outputId": "09c0456e-d313-4176-85ac-f171ae92f53f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c5feaed3313a4f7a9246dfedf5653364",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-28:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+----------+-----+\n",
            "|prediction|churn|\n",
            "+----------+-----+\n",
            "|       0.0|    1|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    1|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    1|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    1|\n",
            "|       0.0|    0|\n",
            "+----------+-----+\n",
            "only showing top 20 rows"
          ]
        }
      ],
      "source": [
        "# Show predictions\n",
        "predictions.select(\"prediction\", \"churn\").show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f8da96f7",
      "metadata": {
        "id": "f8da96f7"
      },
      "source": [
        "**Below we print the coefficients as we did earlier to see if they are any different than zero**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f20ff063",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "95207b42867c44f0bf9c8c014212d877",
            ""
          ]
        },
        "id": "f20ff063",
        "outputId": "86b0143d-956e-4246-c122-fc906318fef2"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "95207b42867c44f0bf9c8c014212d877",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-30:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Coefficients: [-0.005766840139288066,-0.011607216809631143,0.028847941732903025,0.02969447799298724,0.03802321179498868,-0.016963496208451955,-0.010127339613500501,0.008093214541628304,0.004007294767260409,-0.04502621461426905,-0.02256252550701667]\n",
            "Intercept: -1.3610341860912762\n",
            "ROC: Area Under Curve (AUC) = 0.5218774038461532\n",
            "('pincode_index', -0.005766840139288066)\n",
            "('gender_index', -0.011607216809631143)\n",
            "('city_index', 0.028847941732903025)\n",
            "('state_index', 0.02969447799298724)\n",
            "('telecom_partner_index', 0.03802321179498868)\n",
            "('age', -0.016963496208451955)\n",
            "('num_dependents', -0.010127339613500501)\n",
            "('estimated_salary', 0.008093214541628304)\n",
            "('calls_made', 0.004007294767260409)\n",
            "('sms_sent', -0.04502621461426905)\n",
            "('data_used', -0.02256252550701667)"
          ]
        }
      ],
      "source": [
        "# Get the best Logistic Regression model\n",
        "bestModel = cvModel.bestModel\n",
        "\n",
        "# Print the coefficients and intercept for logistic regression\n",
        "print(\"Coefficients: \" + str(bestModel.coefficients))\n",
        "print(\"Intercept: \" + str(bestModel.intercept))\n",
        "\n",
        "# Obtain the receiver-operating characteristic as a DataFrame and areaUnderROC.\n",
        "trainingSummary = bestModel.summary\n",
        "print(\"ROC: Area Under Curve (AUC) = \" + str(trainingSummary.areaUnderROC))\n",
        "\n",
        "# Get the model coefficients\n",
        "coefficients = bestModel.coefficients\n",
        "\n",
        "# Map feature names to coefficients\n",
        "feature_names = assemblerInputs\n",
        "feature_coefficients = list(zip(feature_names, coefficients))\n",
        "\n",
        "for feature in feature_coefficients:\n",
        "    print(feature)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d9553b6",
      "metadata": {
        "id": "8d9553b6"
      },
      "source": [
        "**As you can see the coefficients are now more reasonable than the first iteration** It appears the hyperparameters was set too high in the first instance.\n",
        "\n",
        "**To provide** some more context:\n",
        "\n",
        "The regParam is a regularization parameter. Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function. If regParam is too large, it may result in underfitting, which might push all coefficients towards zero.\n",
        "\n",
        "On the other hand, elasticNetParam corresponds to the Elastic Net mixing parameter. If elasticNetParam = 0, the trained model will be L2 regularization (Ridge). If elasticNetParam = 1, it will be L1 regularization (Lasso).\n",
        "\n",
        "What we did was use a wider range of regParam values and fix elasticNetParam to 0 (which means we're using L2 or Ridge regularization)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "10cd4f57",
      "metadata": {
        "id": "10cd4f57"
      },
      "source": [
        "**With respect to interpretation**\n",
        "\n",
        "- The intercept (−1.3610341860912762) is the log-odds of the 'churn' event when all the independent variables are held at zero. In the context of your logistic regression model, it's not very interpretable because the independent variables can't all be zero at the same time (for example, 'age' can't be zero).\n",
        "\n",
        "- 'pincode_index' ( − 0.005766840139288066): For each one-unit increase in 'pincode_index', the log-odds of 'churn' decreases by approximately 0.00577, given all other variables are held constant.\n",
        "- 'gender_index' (−0.011607216809631143): For each one-unit increase in 'gender_index', the log-odds of 'churn' decreases by approximately 0.0116, given all other variables are held constant.\n",
        "- 'city_index' (0.028847941732903025): For each one-unit increase in 'city_index', the log-odds of 'churn' increases by approximately 0.02885, given all other variables are held constant.\n",
        "- 'state_index' (0.02969447799298724): For each one-unit increase in 'state_index', the log-odds of 'churn' increases by approximately 0.02969, given all other variables are held constant.\n",
        "And so on...\n",
        "\n",
        "It is important to note that the coefficients are based on the scaled features. Therefore, a one-unit change is in terms of the standardized values, not the original scale of the variables.\n",
        "\n",
        "\n",
        "\n",
        "- **The AUC-ROC (0.5218774038461532) is a measure of the overall performance of the model. It ranges from 0.5 to 1, where 0.5 indicates a model that performs no better than random guessing, and 1 indicates a model that performs perfectly. An AUC-ROC of approximately 0.52 suggests that the model's performance is slightly better than random guessing, but there's certainly room for improvement.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "61a37609",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "a65e44b3d16143ebabc66bdc5be5911f",
            ""
          ]
        },
        "id": "61a37609",
        "outputId": "fa08ec83-5201-4c0a-b631-0c35ed4055c3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a65e44b3d16143ebabc66bdc5be5911f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
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              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Performance Summary:\n",
            "Accuracy = 0.80\n",
            "Precision = 0.64\n",
            "Recall = 0.80\n",
            "F1 Score = 0.71\n",
            "AUC = 0.52"
          ]
        }
      ],
      "source": [
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator, MulticlassClassificationEvaluator\n",
        "\n",
        "# Define the evaluators\n",
        "binary_evaluator = BinaryClassificationEvaluator(labelCol='churn')\n",
        "multi_evaluator = MulticlassClassificationEvaluator(labelCol='churn')\n",
        "\n",
        "# Compute the evaluation metrics\n",
        "accuracy = multi_evaluator.evaluate(predictions, {multi_evaluator.metricName: \"accuracy\"})\n",
        "precision = multi_evaluator.evaluate(predictions, {multi_evaluator.metricName: \"weightedPrecision\"})\n",
        "recall = multi_evaluator.evaluate(predictions, {multi_evaluator.metricName: \"weightedRecall\"})\n",
        "f1 = multi_evaluator.evaluate(predictions, {multi_evaluator.metricName: \"f1\"})\n",
        "auc = binary_evaluator.evaluate(predictions)\n",
        "\n",
        "# Print the evaluation metrics\n",
        "print(\"Model Performance Summary:\")\n",
        "print(\"Accuracy = {:.2f}\".format(accuracy))\n",
        "print(\"Precision = {:.2f}\".format(precision))\n",
        "print(\"Recall = {:.2f}\".format(recall))\n",
        "print(\"F1 Score = {:.2f}\".format(f1))\n",
        "print(\"AUC = {:.2f}\".format(auc))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "21cc214c",
      "metadata": {
        "id": "21cc214c"
      },
      "source": [
        "**We print the model performance metrics above. As you can see they are not any different than in the first instance. plus the interpretation is similar as before**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "31f02d82",
      "metadata": {
        "id": "31f02d82"
      },
      "source": [
        "**Below we print the objective history**\n",
        "\n",
        "The objective history represents the value of the objective function (the function being minimized during the optimization process) at each iteration of the model's training process. The values you see are the objective function values at each step of the training process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f0d4d0a8",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "b3a9ef53dfc94327aa8063afd91fbd70",
            ""
          ]
        },
        "id": "f0d4d0a8",
        "outputId": "9197a46d-e3b0-4a31-8737-4305a9b19eb1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b3a9ef53dfc94327aa8063afd91fbd70",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ObjectiveHistory:\n",
            "0.5004024235381358\n",
            "0.499929761068848\n",
            "0.49989544698134775\n",
            "0.49989542872084336\n",
            "0.49989542870625786"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-32:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Extract the summary from the LogisticRegressionModel instance trained\n",
        "trainingSummary = cvModel.bestModel.summary\n",
        "\n",
        "# Obtain the objective per iteration\n",
        "objectiveHistory = trainingSummary.objectiveHistory\n",
        "print(\"ObjectiveHistory:\")\n",
        "for objective in objectiveHistory:\n",
        "    print(objective)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d9b4bf8",
      "metadata": {
        "id": "8d9b4bf8"
      },
      "source": [
        "**Ideally**, we would like to see these values decrease with each iteration, which would indicate that the model is learning and improving. In this case, the values are indeed decreasing, which is a good sign.\n",
        "\n",
        "The fact that the values stop decreasing after the fourth iteration might indicate that the model has reached a point where it cannot improve further given the current data and hyperparameters. This could be due to the model reaching its capacity to learn from the given features, or due to the data not containing enough information to further reduce the objective function."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b5b82f4c",
      "metadata": {
        "id": "b5b82f4c"
      },
      "source": [
        "## Random Forest Implementation"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ecbde7a6",
      "metadata": {
        "id": "ecbde7a6"
      },
      "source": [
        "**As directed** we implement the **Random forest model** as well and proceed to calculate the model performance metrics and make comparisons with the **Logistic Regression model.** This will enable us compare these two models and recommend which is more effective given the dataset and context of the analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9c166db6",
      "metadata": {
        "scrolled": true,
        "colab": {
          "referenced_widgets": [
            "28c8c77e9e974fc8822ea41644afff1f",
            ""
          ]
        },
        "id": "9c166db6",
        "outputId": "7542656b-74df-4d89-fbc5-cd6bcdb1e5dc"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "28c8c77e9e974fc8822ea41644afff1f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-33:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#we initialize the process by provisionin required packages and libraries\n",
        "from pyspark.ml.classification import RandomForestClassifier\n",
        "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n",
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e1393060",
      "metadata": {
        "id": "e1393060"
      },
      "source": [
        "**Next** we implement the **Random Forest** **modeling, hyperparameter tuning** and make **predictions** on our test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "837a5607",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "f3ffc43743fa44b9af66a66e9d774c63",
            ""
          ]
        },
        "id": "837a5607",
        "outputId": "1ee3a7c2-8c21-46fa-8a2b-dceb552411bd"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f3ffc43743fa44b9af66a66e9d774c63",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-34:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Define the Random Forest model\n",
        "rf = RandomForestClassifier(featuresCol=\"scaledFeatures\", labelCol=\"churn\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5e72f939",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "f6f13460495b42ae9ed5220d17815fdc",
            ""
          ]
        },
        "id": "5e72f939",
        "outputId": "721604e7-f76b-444d-852c-b08cfa9e83fe"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f6f13460495b42ae9ed5220d17815fdc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-35:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Define the parameter grid for hyperparameter tuning\n",
        "paramGrid_rf = ParamGridBuilder() \\\n",
        "    .addGrid(rf.numTrees, [10, 20, 30]) \\\n",
        "    .addGrid(rf.maxDepth, [5, 10, 15]) \\\n",
        "    .addGrid(rf.maxBins, [32, 64, 128]) \\\n",
        "    .build()\n",
        "\n",
        "# Define the cross-validation\n",
        "crossval_rf = CrossValidator(estimator=rf,\n",
        "                             estimatorParamMaps=paramGrid_rf,\n",
        "                             evaluator=BinaryClassificationEvaluator(labelCol='churn'),\n",
        "                             numFolds=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "61056e03",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "9a6b08226bd640659a6701285d30098d",
            ""
          ]
        },
        "id": "61056e03",
        "outputId": "0bc5601e-901b-4941-fcf7-1ec343e28a6b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a6b08226bd640659a6701285d30098d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread cell_monitor-36:\n",
            "Traceback (most recent call last):\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/mnt/notebook-env/lib/python3.7/threading.py\", line 870, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in cell_monitor\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "  File \"/mnt/notebook-env/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py\", line 154, in <listcomp>\n",
            "    job_group_filtered_jobs = [job for job in jobs_data if job['jobGroup'] == str(statement_id)]\n",
            "KeyError: 'jobGroup'\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Run cross-validation, and choose the best set of parameters.\n",
        "cvModel_rf = crossval_rf.fit(scaled_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "608f337a",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "d3f352a0dd9648dbb5ecf646a7e72863",
            ""
          ]
        },
        "id": "608f337a",
        "outputId": "ebf81e08-23b2-4d52-bc18-beac8ddad9f5"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d3f352a0dd9648dbb5ecf646a7e72863",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Make predictions on the test data\n",
        "predictions_rf = cvModel_rf.transform(scaled_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5f50ee09",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "1eb5570ad7804ec8b63643874ea78283",
            ""
          ]
        },
        "id": "5f50ee09",
        "outputId": "3d753c7f-def2-4094-d19f-a4ad32a554b4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1eb5570ad7804ec8b63643874ea78283",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+----------+-----+\n",
            "|prediction|churn|\n",
            "+----------+-----+\n",
            "|       1.0|    1|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       1.0|    1|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       1.0|    1|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    0|\n",
            "|       0.0|    1|\n",
            "|       0.0|    0|\n",
            "+----------+-----+\n",
            "only showing top 20 rows"
          ]
        }
      ],
      "source": [
        "# Show predictions\n",
        "predictions_rf.select(\"prediction\", \"churn\").show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "12d54d8a",
      "metadata": {
        "id": "12d54d8a"
      },
      "source": [
        "The output above is as expected."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ab4ebf30",
      "metadata": {
        "id": "ab4ebf30"
      },
      "source": [
        "**Random Forest Model Performance Evaluation Metrics**\n",
        "\n",
        "Below we calculate the performance metrics for the random forest model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "42c50e21",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "80a1578c2fec42038772cf721137a7bf",
            ""
          ]
        },
        "id": "42c50e21",
        "outputId": "c9e14d5b-53b3-4deb-9731-7123b9ce6714"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "80a1578c2fec42038772cf721137a7bf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox()"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Performance Summary\n",
            "Accuracy = 0.904308\n",
            "Precision = 0.914253\n",
            "Recall = 0.904308\n",
            "F1 Score = 0.892045\n",
            "AUC = 0.989055"
          ]
        }
      ],
      "source": [
        "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n",
        "\n",
        "# Initialize the MulticlassClassificationEvaluator\n",
        "evaluator = MulticlassClassificationEvaluator(labelCol=\"churn\")\n",
        "\n",
        "# Compute the classification error on test data.\n",
        "accuracy = evaluator.evaluate(predictions_rf, {evaluator.metricName: \"accuracy\"})\n",
        "precision = evaluator.evaluate(predictions_rf, {evaluator.metricName: \"weightedPrecision\"})\n",
        "recall = evaluator.evaluate(predictions_rf, {evaluator.metricName: \"weightedRecall\"})\n",
        "f1 = evaluator.evaluate(predictions_rf, {evaluator.metricName: \"f1\"})\n",
        "\n",
        "# Binary classification evaluator\n",
        "evaluator_binary = BinaryClassificationEvaluator(labelCol=\"churn\")\n",
        "auc = evaluator_binary.evaluate(predictions_rf, {evaluator_binary.metricName: \"areaUnderROC\"})\n",
        "\n",
        "print(\"Model Performance Summary\")\n",
        "print(\"Accuracy = %g\" % accuracy)\n",
        "print(\"Precision = %g\" % precision)\n",
        "print(\"Recall = %g\" % recall)\n",
        "print(\"F1 Score = %g\" % f1)\n",
        "print(\"AUC = %g\" % auc)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a216ed29",
      "metadata": {
        "id": "a216ed29"
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      "source": [
        "- Accuracy (0.904308): This is the proportion of total predictions that were correct. So, about 90.43% of the time, the model is predicting whether a customer churns correctly.\n",
        "\n",
        "- Precision (0.914253): Precision answers the question \"what proportion of positive identifications were actually correct?\" A precision score of 0.914 means that when the model predicts customer churn, it is correct about 91.43% of the time.\n",
        "\n",
        "- Recall (0.904308): Recall answers the question \"what proportion of actual positives was identified correctly?\" So, the model correctly identifies 90.43% of all actual customer churns.\n",
        "\n",
        "- F1 Score (0.892045): The F1 score is a harmonic mean of precision and recall. An F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0. In this case, an F1 score of 0.892 suggests that the model has fairly high precision and recall.\n",
        "\n",
        "- AUC (0.989055): AUC stands for \"Area under the ROC Curve.\" An AUC score of 1 indicates perfect classifier performance, while an AUC score of 0.5 indicates a model that is as good as random. An AUC of approximately 0.989 suggests that the model does an excellent job distinguishing between customers who churn and those who don't"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d45eb169",
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      "source": [
        "The feature Importances attribute of the trained RandomForestClassifier model gives the importance of each feature in predicting the target variable. This can be useful for understanding which features the model considers most informative. We proceed to extract this key component below:"
      ]
    },
    {
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            "ac809da064a34b7cb3b9fdfed1378c4d",
            ""
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        "id": "3c5f6192",
        "outputId": "bed9b7be-4445-496e-96a1-61e6177ae7a0"
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      "outputs": [
        {
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            "text/plain": [
              "VBox()"
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          "text": [
            "('pincode_index', 0.14294714425806412)\n",
            "('gender_index', 0.023270155269575758)\n",
            "('city_index', 0.056293380275098795)\n",
            "('state_index', 0.0956011433751396)\n",
            "('telecom_partner_index', 0.04746933925398193)\n",
            "('age', 0.11924668501717675)\n",
            "('num_dependents', 0.054331782880906475)\n",
            "('estimated_salary', 0.11578005955582733)\n",
            "('calls_made', 0.11639221636724442)\n",
            "('sms_sent', 0.11124689650985864)\n",
            "('data_used', 0.11742119723712621)"
          ]
        }
      ],
      "source": [
        "# Get the feature importances\n",
        "importances = cvModel_rf.bestModel.featureImportances\n",
        "\n",
        "# Map feature names to importances\n",
        "feature_importances = list(zip(assemblerInputs, importances))\n",
        "\n",
        "for feature in feature_importances:\n",
        "    print(feature)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f81735f5",
      "metadata": {
        "id": "f81735f5"
      },
      "source": [
        "**In terms of interpretation** **~ technical and managerial insights**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "01d085bd",
      "metadata": {
        "id": "01d085bd"
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      "source": [
        "The feature importance values represent the relative contribution of each feature to the prediction model. Specifically, these values indicate how important each feature is in making accurate predictions of the target variable. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature.\n",
        "\n",
        "Looking at the output printed above:\n",
        "\n",
        "- The 'pincode_index' has the highest feature importance score (0.1429), indicating that it is the most influential variable in predicting customer churn in the Random Forest model. This might suggest that customers from certain areas (represented by pin codes) are more likely to churn than others.\n",
        "- The 'data_used' (0.1174), 'calls_made' (0.1164), 'estimated_salary' (0.1158), and 'age' (0.1192) also have relatively high importance, suggesting that these factors significantly influence the churn prediction.\n",
        "- 'gender_index' (0.0233) has the lowest feature importance, indicating it has the least influence on predicting customer churn in the model.\n",
        "\n",
        "It is important to keep in mind that correlation does not equal causation ~ feature importance in this case doesn't imply causality—it just means those are the features that the model found most useful for making accurate predictions, based on the patterns it found in the training data. Also, while pincode, for instance, is an important feature, it doesn't necessarily mean that changing pincode will change churn. It's more likely that pincode is associated with other factors (like different marketing campaigns, regional trends, etc.) that influence churn.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c12efe6",
      "metadata": {
        "id": "0c12efe6"
      },
      "source": [
        "**IN SUMMARY**\n",
        "\n",
        "\n",
        "From a technical point of view, the random forest model appears to have higher prediction performance in comparision to the logistic regression model. Of course this is based on the dataset used in this analysis.\n",
        "\n",
        "From our random forest features' importance scores, it apears that a number of variables features (outlined above) are worthy of further investigation as their scores/estimates suggest they play an important role in churn prediction.\n",
        "\n",
        "By and large, a certain threshold and balance of domain knowledge and technical experience hold significance relevance to making sense of the numbers estimates from this research.  \n",
        "\n",
        "Nonetheless, If we had to make a recommendation to the technical and managerial team of our client's organization, we would likely recomend the Random Forest model for extensive further and rigorous evaluation and analysis after which it can then be adopted and deployed at a production level."
      ]
    },
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      "execution_count": null,
      "id": "f1062a04",
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