{ "cells": [ { "cell_type": "markdown", "id": "b51bd473-3278-4466-a058-b2e4b9f30d8e", "metadata": {}, "source": [ "# Customer Churn Prediction, Risk Scoring, and Interactive Pivot Dashboard\n", "\n", "**Client portfolio project prepared for DataScienceConsultingPro.com**\n", "\n", "This notebook presents a complete customer churn analytics project using real Telco Customer Churn data. It demonstrates how raw customer data can be cleaned, explored, modelled, scored, summarized with pivot tables, visualized with charts, and translated into practical business recommendations for customer retention teams.\n", "\n", "## Brief Introduction\n", "\n", "Customer churn occurs when a customer cancels a subscription, leaves a service, switches to a competitor, or stops doing business with a company. For subscription-based and service-based businesses, churn can reduce recurring revenue, increase customer acquisition costs, and weaken customer lifetime value.\n", "\n", "This project uses customer profile, contract, service, billing, and payment data to estimate churn risk. The final output helps a business identify customers who may need retention action before they leave.\n", "\n", "## Aim of the Project\n", "\n", "The aim of this project is to build a practical customer churn prediction workflow that helps a business identify high-risk customers, understand key churn drivers, and support better retention decisions using machine learning, pivot tables, charts, and an interactive dashboard.\n", "\n", "## Project Objectives\n", "\n", "The objectives of this project are to:\n", "\n", "1. Load and inspect real customer churn data.\n", "2. Clean the dataset and prepare it for analysis and modelling.\n", "3. Create useful business features such as tenure groups, customer value groups, and churn flags.\n", "4. Explore churn patterns using graphs, summary statistics, and pivot tables.\n", "5. Train and compare multiple machine learning models.\n", "6. Score every customer using predicted churn probability.\n", "7. Group customers into Low Risk, Medium Risk, and High Risk bands.\n", "8. Build real pivot tables directly inside the Jupyter Notebook.\n", "9. Provide an interactive pivot dashboard without using `ipywidgets`, `VBox`, or `@jupyter-widgets/controls`.\n", "10. Export client-ready outputs for reporting, retention planning, and business decision-making.\n", "\n", "## Compatibility Note\n", "\n", "This notebook intentionally avoids `ipywidgets`, `VBox`, and `@jupyter-widgets/controls`. The interactive pivot dashboard uses plain HTML and JavaScript, which helps prevent common JupyterLab widget-version errors while keeping the notebook easy to open, run, and review.\n" ] }, { "cell_type": "markdown", "id": "cc1cb071", "metadata": {}, "source": [ "## 2. Import Libraries and Load the Raw Data\n", "\n", "This section imports the main analysis libraries and loads the Telco Customer Churn dataset. The notebook first checks whether the CSV file is available locally. If it is not available, it downloads the public CSV file online and saves a local copy." ] }, { "cell_type": "code", "execution_count": 22, "id": "85d2d27d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded local dataset: 01_raw_data_telco_customer_churn.csv\n" ] }, { "data": { "text/html": [ "
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customerIDgenderSeniorCitizenPartnerDependentstenurePhoneServiceMultipleLinesInternetServiceOnlineSecurity...DeviceProtectionTechSupportStreamingTVStreamingMoviesContractPaperlessBillingPaymentMethodMonthlyChargesTotalChargesChurn
07590-VHVEGFemale0YesNo1NoNo phone serviceDSLNo...NoNoNoNoMonth-to-monthYesElectronic check29.8529.85No
15575-GNVDEMale0NoNo34YesNoDSLYes...YesNoNoNoOne yearNoMailed check56.951889.5No
23668-QPYBKMale0NoNo2YesNoDSLYes...NoNoNoNoMonth-to-monthYesMailed check53.85108.15Yes
37795-CFOCWMale0NoNo45NoNo phone serviceDSLYes...YesYesNoNoOne yearNoBank transfer (automatic)42.301840.75No
49237-HQITUFemale0NoNo2YesNoFiber opticNo...NoNoNoNoMonth-to-monthYesElectronic check70.70151.65Yes
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5 rows × 21 columns

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" ], "text/plain": [ " customerID gender SeniorCitizen Partner Dependents tenure PhoneService \\\n", "0 7590-VHVEG Female 0 Yes No 1 No \n", "1 5575-GNVDE Male 0 No No 34 Yes \n", "2 3668-QPYBK Male 0 No No 2 Yes \n", "3 7795-CFOCW Male 0 No No 45 No \n", "4 9237-HQITU Female 0 No No 2 Yes \n", "\n", " MultipleLines InternetService OnlineSecurity ... DeviceProtection \\\n", "0 No phone service DSL No ... No \n", "1 No DSL Yes ... Yes \n", "2 No DSL Yes ... No \n", "3 No phone service DSL Yes ... Yes \n", "4 No Fiber optic No ... No \n", "\n", " TechSupport StreamingTV StreamingMovies Contract PaperlessBilling \\\n", "0 No No No Month-to-month Yes \n", "1 No No No One year No \n", "2 No No No Month-to-month Yes \n", "3 Yes No No One year No \n", "4 No No No Month-to-month Yes \n", "\n", " PaymentMethod MonthlyCharges TotalCharges Churn \n", "0 Electronic check 29.85 29.85 No \n", "1 Mailed check 56.95 1889.5 No \n", "2 Mailed check 53.85 108.15 Yes \n", "3 Bank transfer (automatic) 42.30 1840.75 No \n", "4 Electronic check 70.70 151.65 Yes \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Human note: pandas and numpy handle the dataset, while scikit-learn handles the machine learning workflow.\n", "import pandas as pd\n", "import numpy as np\n", "from pathlib import Path\n", "import warnings\n", "from IPython.display import display, HTML\n", "\n", "# Human note: These imports support model building, preprocessing, evaluation, and exporting the trained model.\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix\n", "from sklearn.impute import SimpleImputer\n", "import joblib\n", "import plotly.express as px\n", "\n", "# Hide FutureWarning messages from pandas/plotly\n", "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n", "\n", "# Human note: The notebook uses the local CSV when available. This makes it reliable for client download packages.\n", "DATA_FILE = Path('01_raw_data_telco_customer_churn.csv')\n", "ONLINE_URL = 'https://raw.githubusercontent.com/IBM/telco-customer-churn-on-icp4d/master/data/Telco-Customer-Churn.csv'\n", "\n", "if DATA_FILE.exists():\n", " df = pd.read_csv(DATA_FILE)\n", " print('Loaded local dataset:', DATA_FILE)\n", "else:\n", " df = pd.read_csv(ONLINE_URL)\n", " df.to_csv(DATA_FILE, index=False)\n", " print('Downloaded online dataset and saved a local copy.')\n", "\n", "# Human note: Displaying the first rows confirms that the dataset loaded correctly.\n", "df.head()" ] }, { "cell_type": "markdown", "id": "cbb2422d", "metadata": {}, "source": [ "## 3. Initial Data Audit\n", "\n", "This cell checks the size, data types, missing values, and descriptive summary of the dataset. This is useful before cleaning because it shows which columns need formatting or preparation." ] }, { "cell_type": "code", "execution_count": 23, "id": "bb06da3b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rows: 7043\n", "Columns: 21\n", "\n", "RangeIndex: 7043 entries, 0 to 7042\n", "Data columns (total 21 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 customerID 7043 non-null object \n", " 1 gender 7043 non-null object \n", " 2 SeniorCitizen 7043 non-null int64 \n", " 3 Partner 7043 non-null object \n", " 4 Dependents 7043 non-null object \n", " 5 tenure 7043 non-null int64 \n", " 6 PhoneService 7043 non-null object \n", " 7 MultipleLines 7043 non-null object \n", " 8 InternetService 7043 non-null object \n", " 9 OnlineSecurity 7043 non-null object \n", " 10 OnlineBackup 7043 non-null object \n", " 11 DeviceProtection 7043 non-null object \n", " 12 TechSupport 7043 non-null object \n", " 13 StreamingTV 7043 non-null object \n", " 14 StreamingMovies 7043 non-null object \n", " 15 Contract 7043 non-null object \n", " 16 PaperlessBilling 7043 non-null object \n", " 17 PaymentMethod 7043 non-null object \n", " 18 MonthlyCharges 7043 non-null float64\n", " 19 TotalCharges 7043 non-null object \n", " 20 Churn 7043 non-null object \n", "dtypes: float64(1), int64(2), object(18)\n", "memory usage: 1.1+ MB\n" ] }, { "data": { "text/plain": [ "None" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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countuniquetopfreqmeanstdmin25%50%75%max
customerID704370437590-VHVEG1NaNNaNNaNNaNNaNNaNNaN
gender70432Male3555NaNNaNNaNNaNNaNNaNNaN
SeniorCitizen7043.0NaNNaNNaN0.1621470.3686120.00.00.00.01.0
Partner70432No3641NaNNaNNaNNaNNaNNaNNaN
Dependents70432No4933NaNNaNNaNNaNNaNNaNNaN
tenure7043.0NaNNaNNaN32.37114924.5594810.09.029.055.072.0
PhoneService70432Yes6361NaNNaNNaNNaNNaNNaNNaN
MultipleLines70433No3390NaNNaNNaNNaNNaNNaNNaN
InternetService70433Fiber optic3096NaNNaNNaNNaNNaNNaNNaN
OnlineSecurity70433No3498NaNNaNNaNNaNNaNNaNNaN
OnlineBackup70433No3088NaNNaNNaNNaNNaNNaNNaN
DeviceProtection70433No3095NaNNaNNaNNaNNaNNaNNaN
TechSupport70433No3473NaNNaNNaNNaNNaNNaNNaN
StreamingTV70433No2810NaNNaNNaNNaNNaNNaNNaN
StreamingMovies70433No2785NaNNaNNaNNaNNaNNaNNaN
Contract70433Month-to-month3875NaNNaNNaNNaNNaNNaNNaN
PaperlessBilling70432Yes4171NaNNaNNaNNaNNaNNaNNaN
PaymentMethod70434Electronic check2365NaNNaNNaNNaNNaNNaNNaN
MonthlyCharges7043.0NaNNaNNaN64.76169230.09004718.2535.570.3589.85118.75
TotalCharges7043653111NaNNaNNaNNaNNaNNaNNaN
Churn70432No5174NaNNaNNaNNaNNaNNaNNaN
\n", "
" ], "text/plain": [ " count unique top freq mean std \\\n", "customerID 7043 7043 7590-VHVEG 1 NaN NaN \n", "gender 7043 2 Male 3555 NaN NaN \n", "SeniorCitizen 7043.0 NaN NaN NaN 0.162147 0.368612 \n", "Partner 7043 2 No 3641 NaN NaN \n", "Dependents 7043 2 No 4933 NaN NaN \n", "tenure 7043.0 NaN NaN NaN 32.371149 24.559481 \n", "PhoneService 7043 2 Yes 6361 NaN NaN \n", "MultipleLines 7043 3 No 3390 NaN NaN \n", "InternetService 7043 3 Fiber optic 3096 NaN NaN \n", "OnlineSecurity 7043 3 No 3498 NaN NaN \n", "OnlineBackup 7043 3 No 3088 NaN NaN \n", "DeviceProtection 7043 3 No 3095 NaN NaN \n", "TechSupport 7043 3 No 3473 NaN NaN \n", "StreamingTV 7043 3 No 2810 NaN NaN \n", "StreamingMovies 7043 3 No 2785 NaN NaN \n", "Contract 7043 3 Month-to-month 3875 NaN NaN \n", "PaperlessBilling 7043 2 Yes 4171 NaN NaN \n", "PaymentMethod 7043 4 Electronic check 2365 NaN NaN \n", "MonthlyCharges 7043.0 NaN NaN NaN 64.761692 30.090047 \n", "TotalCharges 7043 6531 11 NaN NaN \n", "Churn 7043 2 No 5174 NaN NaN \n", "\n", " min 25% 50% 75% max \n", "customerID NaN NaN NaN NaN NaN \n", "gender NaN NaN NaN NaN NaN \n", "SeniorCitizen 0.0 0.0 0.0 0.0 1.0 \n", "Partner NaN NaN NaN NaN NaN \n", "Dependents NaN NaN NaN NaN NaN \n", "tenure 0.0 9.0 29.0 55.0 72.0 \n", "PhoneService NaN NaN NaN NaN NaN \n", "MultipleLines NaN NaN NaN NaN NaN \n", "InternetService NaN NaN NaN NaN NaN \n", "OnlineSecurity NaN NaN NaN NaN NaN \n", "OnlineBackup NaN NaN NaN NaN NaN \n", "DeviceProtection NaN NaN NaN NaN NaN \n", "TechSupport NaN NaN NaN NaN NaN \n", "StreamingTV NaN NaN NaN NaN NaN \n", "StreamingMovies NaN NaN NaN NaN NaN \n", "Contract NaN NaN NaN NaN NaN \n", "PaperlessBilling NaN NaN NaN NaN NaN \n", "PaymentMethod NaN NaN NaN NaN NaN \n", "MonthlyCharges 18.25 35.5 70.35 89.85 118.75 \n", "TotalCharges NaN NaN NaN NaN NaN \n", "Churn NaN NaN NaN NaN NaN " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Human note: The shape confirms the number of customer records and variables available for analysis.\n", "print('Rows:', df.shape[0])\n", "print('Columns:', df.shape[1])\n", "\n", "# Human note: The info view shows data types and non-null counts for each column.\n", "display(df.info())\n", "\n", "# Human note: This summary gives a quick review of numeric and categorical columns.\n", "display(df.describe(include='all').T.head(25))" ] }, { "cell_type": "markdown", "id": "725d1fa0", "metadata": {}, "source": [ "## 4. Data Cleaning and Feature Engineering\n", "\n", "This section prepares the raw customer data for analysis and modelling. The key cleaning task is converting `TotalCharges` into a numeric variable. The new features make the results easier to interpret in charts, pivot tables, and client reports." ] }, { "cell_type": "code", "execution_count": 24, "id": "d4e694d0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customerIDtenureContractPaymentMethodInternetServiceMonthlyChargesTotalChargesChurnChurn_FlagTenure_GroupMonthly_Charge_GroupCustomer_Value_Group
07590-VHVEG1Month-to-monthElectronic checkDSL29.8529.85No00-6 monthsLowLow Value
15575-GNVDE34One yearMailed checkDSL56.951889.50No025-48 monthsMediumHigh Value
23668-QPYBK2Month-to-monthMailed checkDSL53.85108.15Yes10-6 monthsMediumLow Value
37795-CFOCW45One yearBank transfer (automatic)DSL42.301840.75No025-48 monthsMediumHigh Value
49237-HQITU2Month-to-monthElectronic checkFiber optic70.70151.65Yes10-6 monthsHighLow Value
\n", "
" ], "text/plain": [ " customerID tenure Contract PaymentMethod \\\n", "0 7590-VHVEG 1 Month-to-month Electronic check \n", "1 5575-GNVDE 34 One year Mailed check \n", "2 3668-QPYBK 2 Month-to-month Mailed check \n", "3 7795-CFOCW 45 One year Bank transfer (automatic) \n", "4 9237-HQITU 2 Month-to-month Electronic check \n", "\n", " InternetService MonthlyCharges TotalCharges Churn Churn_Flag \\\n", "0 DSL 29.85 29.85 No 0 \n", "1 DSL 56.95 1889.50 No 0 \n", "2 DSL 53.85 108.15 Yes 1 \n", "3 DSL 42.30 1840.75 No 0 \n", "4 Fiber optic 70.70 151.65 Yes 1 \n", "\n", " Tenure_Group Monthly_Charge_Group Customer_Value_Group \n", "0 0-6 months Low Low Value \n", "1 25-48 months Medium High Value \n", "2 0-6 months Medium Low Value \n", "3 25-48 months Medium High Value \n", "4 0-6 months High Low Value " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Human note: TotalCharges sometimes appears as text, so it must be converted into a numeric field.\n", "df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')\n", "\n", "# Human note: Missing TotalCharges values are filled with the median to avoid removing useful customer records.\n", "df['TotalCharges'] = df['TotalCharges'].fillna(df['TotalCharges'].median())\n", "\n", "# Human note: Churn_Flag converts Yes/No churn labels into 1/0 values for modelling and pivot tables.\n", "df['Churn_Flag'] = (df['Churn'].astype(str).str.lower() == 'yes').astype(int)\n", "\n", "# Human note: Tenure groups help managers see whether newer or older customers have different churn patterns.\n", "df['Tenure_Group'] = pd.cut(\n", " df['tenure'],\n", " bins=[-1, 6, 12, 24, 48, 72],\n", " labels=['0-6 months', '7-12 months', '13-24 months', '25-48 months', '49-72 months']\n", ")\n", "\n", "# Human note: Monthly charge groups make billing-related churn easier to summarize.\n", "df['Monthly_Charge_Group'] = pd.cut(\n", " df['MonthlyCharges'],\n", " bins=[0, 35, 70, 105, 150],\n", " labels=['Low', 'Medium', 'High', 'Very High']\n", ")\n", "\n", "# Human note: Customer value groups divide customers into four value bands based on total charges.\n", "df['Customer_Value_Group'] = pd.qcut(\n", " df['TotalCharges'].rank(method='first'),\n", " q=4,\n", " labels=['Low Value', 'Medium Value', 'High Value', 'Very High Value']\n", ")\n", "\n", "# Human note: This preview confirms that the new business-friendly fields were created correctly.\n", "df[['customerID','tenure','Contract','PaymentMethod','InternetService','MonthlyCharges','TotalCharges','Churn','Churn_Flag','Tenure_Group','Monthly_Charge_Group','Customer_Value_Group']].head()" ] }, { "cell_type": "markdown", "id": "a2136e7b", "metadata": {}, "source": [ "## 5. Graphs: Exploratory Analysis\n", "\n", "The following graphs help explain churn patterns before model building. These visuals are useful for managers because they connect customer behaviour, contract type, payment method, and charges to churn outcomes." ] }, { "cell_type": "code", "execution_count": 25, "id": "db364b5e", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "bingroup": "x", "hovertemplate": "Churn=No
Contract=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Human note: This graph compares churn across contract types.\n", "# It helps show whether month-to-month customers behave differently from longer-contract customers.\n", "fig = px.histogram(df, x='Contract', color='Churn', barmode='group', title='Churn by Contract Type')\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 26, "id": "55571f9a", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "bingroup": "x", "hovertemplate": "Churn=No
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Human note: This graph checks whether churn varies by payment method.\n", "# It can help identify billing methods associated with higher customer loss.\n", "fig = px.histogram(df, x='PaymentMethod', color='Churn', barmode='group', title='Churn by Payment Method')\n", "fig.update_xaxes(tickangle=25)\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 27, "id": "4e485bda", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "Churn=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Human note: This box plot compares monthly charges for customers who churned and those who stayed.\n", "# It helps reveal whether higher charges may be linked to churn risk.\n", "fig = px.box(df, x='Churn', y='MonthlyCharges', color='Churn', title='Monthly Charges by Churn Status')\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "d3776ffa", "metadata": {}, "source": [ "## 6. Machine Learning Model Development\n", "\n", "This section trains and compares three classification models: Logistic Regression, Random Forest, and Gradient Boosting. The model with the highest ROC AUC is selected for customer risk scoring." ] }, { "cell_type": "code", "execution_count": 28, "id": "601f4d30", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ModelAccuracyPrecisionRecallF1 ScoreROC AUC
0Logistic Regression0.7972740.6536310.5010710.5672730.848553
2Gradient Boosting0.8006810.6676300.4946470.5682660.845948
1Random Forest0.7825100.6213870.4603850.5289050.824386
\n", "
" ], "text/plain": [ " Model Accuracy Precision Recall F1 Score ROC AUC\n", "0 Logistic Regression 0.797274 0.653631 0.501071 0.567273 0.848553\n", "2 Gradient Boosting 0.800681 0.667630 0.494647 0.568266 0.845948\n", "1 Random Forest 0.782510 0.621387 0.460385 0.528905 0.824386" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Best model selected: Logistic Regression\n" ] }, { "data": { "text/html": [ "
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Predicted StayedPredicted Churned
Actual Stayed1170124
Actual Churned233234
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" ], "text/plain": [ " Predicted Stayed Predicted Churned\n", "Actual Stayed 1170 124\n", "Actual Churned 233 234" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The target variable is Churn_Flag. Customer ID and final output fields are excluded from predictors.\n", "features = [c for c in df.columns if c not in ['customerID','Churn','Churn_Flag','Risk_Band','Churn_Probability']]\n", "X = df[features]\n", "y = df['Churn_Flag']\n", "\n", "# Categorical and numeric columns need different preprocessing steps before modelling.\n", "categorical_features = [c for c in features if X[c].dtype == 'object' or str(X[c].dtype) == 'category']\n", "numeric_features = [c for c in features if c not in categorical_features]\n", "\n", "# Numeric variables are imputed and scaled. Categorical variables are imputed and one-hot encoded.\n", "preprocess = ColumnTransformer([\n", " ('num', Pipeline([\n", " ('imputer', SimpleImputer(strategy='median')),\n", " ('scaler', StandardScaler())\n", " ]), numeric_features),\n", " ('cat', Pipeline([\n", " ('imputer', SimpleImputer(strategy='most_frequent')),\n", " ('encoder', OneHotEncoder(handle_unknown='ignore'))\n", " ]), categorical_features)\n", "])\n", "\n", "# Multiple models are compared so the project does not depend on a single modelling approach.\n", "models = {\n", " 'Logistic Regression': LogisticRegression(max_iter=1000),\n", " 'Random Forest': RandomForestClassifier(n_estimators=150, random_state=42, class_weight='balanced'),\n", " 'Gradient Boosting': GradientBoostingClassifier(random_state=42)\n", "}\n", "\n", "# Stratification preserves the churn/non-churn balance in both training and testing data.\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=.25, random_state=42, stratify=y\n", ")\n", "\n", "results = []\n", "fitted_models = {}\n", "\n", "# Each model is trained, tested, and evaluated using common classification metrics.\n", "for name, model in models.items():\n", " pipe = Pipeline([('preprocess', preprocess), ('model', model)])\n", " pipe.fit(X_train, y_train)\n", " pred = pipe.predict(X_test)\n", " proba = pipe.predict_proba(X_test)[:, 1]\n", "\n", " results.append({\n", " 'Model': name,\n", " 'Accuracy': accuracy_score(y_test, pred),\n", " 'Precision': precision_score(y_test, pred),\n", " 'Recall': recall_score(y_test, pred),\n", " 'F1 Score': f1_score(y_test, pred),\n", " 'ROC AUC': roc_auc_score(y_test, proba)\n", " })\n", " fitted_models[name] = pipe\n", "\n", "# The comparison table helps identify the strongest model for churn risk scoring.\n", "model_metrics = pd.DataFrame(results).sort_values('ROC AUC', ascending=False)\n", "display(model_metrics)\n", "\n", "# The best model is selected using ROC AUC because churn prediction needs strong separation between churners and non-churners.\n", "best_model_name = model_metrics.iloc[0]['Model']\n", "best_model = fitted_models[best_model_name]\n", "print('Best model selected:', best_model_name)\n", "\n", "# The confusion matrix shows the number of correct and incorrect predictions on the test data.\n", "y_pred_best = best_model.predict(X_test)\n", "cm = pd.DataFrame(\n", " confusion_matrix(y_test, y_pred_best),\n", " index=['Actual Stayed', 'Actual Churned'],\n", " columns=['Predicted Stayed', 'Predicted Churned']\n", ")\n", "display(cm)" ] }, { "cell_type": "code", "execution_count": 29, "id": "ec90254b", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "variable=Accuracy
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This grouped bar chart makes model performance easier to compare visually.\n", "fig = px.bar(\n", " model_metrics,\n", " x='Model',\n", " y=['Accuracy','Precision','Recall','F1 Score','ROC AUC'],\n", " barmode='group',\n", " title='Model Comparison'\n", ")\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "56bc52f6", "metadata": {}, "source": [ "## 7. Customer Risk Scoring\n", "\n", "The selected model is used to estimate churn probability for every customer. Customers are then grouped into risk bands so managers can prioritize retention action." ] }, { "cell_type": "code", "execution_count": 30, "id": "0cd9a8b4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customerIDContractPaymentMethodInternetServicetenureMonthlyChargesTotalChargesChurnChurn_ProbabilityRisk_Band
19769497-QCMMSMonth-to-monthElectronic checkFiber optic193.5593.55Yes0.906285High Risk
48009300-AGZNLMonth-to-monthElectronic checkFiber optic194.0094.00Yes0.905689High Risk
33805178-LMXOPMonth-to-monthElectronic checkFiber optic195.1095.10Yes0.905204High Risk
37494424-TKOPWMonth-to-monthElectronic checkFiber optic293.85196.75Yes0.903803High Risk
22087216-EWTRSMonth-to-monthElectronic checkFiber optic1100.80100.80Yes0.903715High Risk
63682720-WGKHPMonth-to-monthElectronic checkFiber optic294.00181.70Yes0.903607High Risk
14107024-OHCCKMonth-to-monthElectronic checkFiber optic293.85170.85Yes0.902992High Risk
9971374-DMZUIMonth-to-monthElectronic checkFiber optic494.30424.45Yes0.902330High Risk
59895567-WSELEMonth-to-monthElectronic checkFiber optic394.60279.55Yes0.901667High Risk
31595150-ITWWBMonth-to-monthElectronic checkFiber optic394.85335.75No0.900272High Risk
68941400-MMYXYMonth-to-monthElectronic checkFiber optic3105.90334.65Yes0.896681High Risk
3018098-LLAZXMonth-to-monthElectronic checkFiber optic495.45396.10Yes0.896367High Risk
32098149-RSOUNMonth-to-monthElectronic checkFiber optic193.8593.85Yes0.887070High Risk
25774910-GMJOTMonth-to-monthElectronic checkFiber optic194.6094.60Yes0.885896High Risk
68660295-PPHDOMonth-to-monthElectronic checkFiber optic195.4595.45Yes0.885511High Risk
64825419-JPRRNMonth-to-monthElectronic checkFiber optic1101.45101.45Yes0.884240High Risk
59336496-SLWHQMonth-to-monthElectronic checkFiber optic3105.00294.45Yes0.883020High Risk
11487851-WZEKYMonth-to-monthElectronic checkFiber optic295.15196.90Yes0.882541High Risk
62152446-BEGGBMonth-to-monthElectronic checkFiber optic698.25560.60Yes0.882025High Risk
9693158-MOERKMonth-to-monthElectronic checkFiber optic296.00174.80Yes0.881175High Risk
52823049-SOLAYMonth-to-monthElectronic checkFiber optic395.20292.85Yes0.881134High Risk
27976023-YEBUPMonth-to-monthElectronic checkFiber optic3100.95329.95Yes0.880000High Risk
62329681-OXGVCMonth-to-monthElectronic checkFiber optic5100.50514.00Yes0.879726High Risk
21844695-WJZUEMonth-to-monthElectronic checkFiber optic288.55179.25Yes0.878290High Risk
45851069-XAIEMMonth-to-monthElectronic checkFiber optic185.0585.05Yes0.878046High Risk
\n", "
" ], "text/plain": [ " customerID Contract PaymentMethod InternetService tenure \\\n", "1976 9497-QCMMS Month-to-month Electronic check Fiber optic 1 \n", "4800 9300-AGZNL Month-to-month Electronic check Fiber optic 1 \n", "3380 5178-LMXOP Month-to-month Electronic check Fiber optic 1 \n", "3749 4424-TKOPW Month-to-month Electronic check Fiber optic 2 \n", "2208 7216-EWTRS Month-to-month Electronic check Fiber optic 1 \n", "6368 2720-WGKHP Month-to-month Electronic check Fiber optic 2 \n", "1410 7024-OHCCK Month-to-month Electronic check Fiber optic 2 \n", "997 1374-DMZUI Month-to-month Electronic check Fiber optic 4 \n", "5989 5567-WSELE Month-to-month Electronic check Fiber optic 3 \n", "3159 5150-ITWWB Month-to-month Electronic check Fiber optic 3 \n", "6894 1400-MMYXY Month-to-month Electronic check Fiber optic 3 \n", "301 8098-LLAZX Month-to-month Electronic check Fiber optic 4 \n", "3209 8149-RSOUN Month-to-month Electronic check Fiber optic 1 \n", "2577 4910-GMJOT Month-to-month Electronic check Fiber optic 1 \n", "6866 0295-PPHDO Month-to-month Electronic check Fiber optic 1 \n", "6482 5419-JPRRN Month-to-month Electronic check Fiber optic 1 \n", "5933 6496-SLWHQ Month-to-month Electronic check Fiber optic 3 \n", "1148 7851-WZEKY Month-to-month Electronic check Fiber optic 2 \n", "6215 2446-BEGGB Month-to-month Electronic check Fiber optic 6 \n", "969 3158-MOERK Month-to-month Electronic check Fiber optic 2 \n", "5282 3049-SOLAY Month-to-month Electronic check Fiber optic 3 \n", "2797 6023-YEBUP Month-to-month Electronic check Fiber optic 3 \n", "6232 9681-OXGVC Month-to-month Electronic check Fiber optic 5 \n", "2184 4695-WJZUE Month-to-month Electronic check Fiber optic 2 \n", "4585 1069-XAIEM Month-to-month Electronic check Fiber optic 1 \n", "\n", " MonthlyCharges TotalCharges Churn Churn_Probability Risk_Band \n", "1976 93.55 93.55 Yes 0.906285 High Risk \n", "4800 94.00 94.00 Yes 0.905689 High Risk \n", "3380 95.10 95.10 Yes 0.905204 High Risk \n", "3749 93.85 196.75 Yes 0.903803 High Risk \n", "2208 100.80 100.80 Yes 0.903715 High Risk \n", "6368 94.00 181.70 Yes 0.903607 High Risk \n", "1410 93.85 170.85 Yes 0.902992 High Risk \n", "997 94.30 424.45 Yes 0.902330 High Risk \n", "5989 94.60 279.55 Yes 0.901667 High Risk \n", "3159 94.85 335.75 No 0.900272 High Risk \n", "6894 105.90 334.65 Yes 0.896681 High Risk \n", "301 95.45 396.10 Yes 0.896367 High Risk \n", "3209 93.85 93.85 Yes 0.887070 High Risk \n", "2577 94.60 94.60 Yes 0.885896 High Risk \n", "6866 95.45 95.45 Yes 0.885511 High Risk \n", "6482 101.45 101.45 Yes 0.884240 High Risk \n", "5933 105.00 294.45 Yes 0.883020 High Risk \n", "1148 95.15 196.90 Yes 0.882541 High Risk \n", "6215 98.25 560.60 Yes 0.882025 High Risk \n", "969 96.00 174.80 Yes 0.881175 High Risk \n", "5282 95.20 292.85 Yes 0.881134 High Risk \n", "2797 100.95 329.95 Yes 0.880000 High Risk \n", "6232 100.50 514.00 Yes 0.879726 High Risk \n", "2184 88.55 179.25 Yes 0.878290 High Risk \n", "4585 85.05 85.05 Yes 0.878046 High Risk " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Churn_Probability gives each customer an estimated risk of leaving.\n", "df['Churn_Probability'] = best_model.predict_proba(X)[:, 1]\n", "\n", "# Risk_Band turns model probabilities into business-friendly action groups.\n", "df['Risk_Band'] = pd.cut(\n", " df['Churn_Probability'],\n", " bins=[-0.001, .33, .66, 1.0],\n", " labels=['Low Risk', 'Medium Risk', 'High Risk']\n", ")\n", "\n", "# This customer-level output is useful for retention teams because it can be sorted by highest risk.\n", "risk_scores = df[['customerID','Contract','PaymentMethod','InternetService','tenure','MonthlyCharges','TotalCharges','Churn','Churn_Probability','Risk_Band']]\n", "display(risk_scores.sort_values('Churn_Probability', ascending=False).head(25))" ] }, { "cell_type": "code", "execution_count": 31, "id": "fca9fe40", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "bingroup": "x", "hovertemplate": "Churn=No
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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This graph compares model risk bands with actual churn labels.\n", "# It helps check whether the high-risk band captures many real churn cases.\n", "fig = px.histogram(df, x='Risk_Band', color='Churn', barmode='group', title='Risk Band Distribution by Actual Churn')\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "340e13db", "metadata": {}, "source": [ "## 8. Real Pivot Tables in Jupyter Notebook\n", "\n", "These are standard pandas pivot tables displayed directly inside the notebook. They are reliable because they do not require Jupyter widgets, extensions, or VBox controls." ] }, { "cell_type": "code", "execution_count": 32, "id": "2911251b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Risk_BandLow RiskMedium RiskHigh Risk
Contract
Month-to-month14881638749
One year1423500
Two year169500
\n", "
" ], "text/plain": [ "Risk_Band Low Risk Medium Risk High Risk\n", "Contract \n", "Month-to-month 1488 1638 749\n", "One year 1423 50 0\n", "Two year 1695 0 0" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Pivot 1 counts customers by contract type and model risk band.\n", "# Business use: shows where the largest high-risk customer groups are located.\n", "pivot_1 = pd.pivot_table(\n", " df,\n", " index='Contract',\n", " columns='Risk_Band',\n", " values='customerID',\n", " aggfunc='count',\n", " fill_value=0\n", ")\n", "pivot_1" ] }, { "cell_type": "code", "execution_count": 33, "id": "ff98b038", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ContractMonth-to-monthOne yearTwo year
PaymentMethod
Bank transfer (automatic)0.3290.1010.030
Credit card (automatic)0.3330.0950.027
Electronic check0.5370.1810.069
Mailed check0.3160.0740.021
\n", "
" ], "text/plain": [ "Contract Month-to-month One year Two year\n", "PaymentMethod \n", "Bank transfer (automatic) 0.329 0.101 0.030\n", "Credit card (automatic) 0.333 0.095 0.027\n", "Electronic check 0.537 0.181 0.069\n", "Mailed check 0.316 0.074 0.021" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Pivot 2 shows average churn probability by payment method and contract type.\n", "# Business use: identifies billing and contract combinations with higher churn risk.\n", "pivot_2 = pd.pivot_table(\n", " df,\n", " index='PaymentMethod',\n", " columns='Contract',\n", " values='Churn_Probability',\n", " aggfunc='mean'\n", ").round(3)\n", "pivot_2" ] }, { "cell_type": "code", "execution_count": 34, "id": "958ecf38", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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InternetServiceDSLFiber opticNo
Tenure_Group
0-6 months0.4820.7420.222
7-12 months0.2250.6110.086
13-24 months0.1960.4880.038
25-48 months0.1080.3660.018
49-72 months0.0430.1710.014
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" ], "text/plain": [ "InternetService DSL Fiber optic No\n", "Tenure_Group \n", "0-6 months 0.482 0.742 0.222\n", "7-12 months 0.225 0.611 0.086\n", "13-24 months 0.196 0.488 0.038\n", "25-48 months 0.108 0.366 0.018\n", "49-72 months 0.043 0.171 0.014" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Pivot 3 shows actual churn rate by tenure group and internet service.\n", "# Business use: helps compare early-life churn and service-related churn patterns.\n", "pivot_3 = pd.pivot_table(\n", " df,\n", " index='Tenure_Group',\n", " columns='InternetService',\n", " values='Churn_Flag',\n", " aggfunc='mean'\n", ").round(3)\n", "pivot_3" ] }, { "cell_type": "code", "execution_count": 35, "id": "03434a75", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ContractMonth-to-monthOne yearTwo year
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Low Risk48.7663.9460.77
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High Risk81.79NaNNaN
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" ], "text/plain": [ "Contract Month-to-month One year Two year\n", "Risk_Band \n", "Low Risk 48.76 63.94 60.77\n", "Medium Risk 75.38 96.72 NaN\n", "High Risk 81.79 NaN NaN" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Pivot 4 shows average monthly charges by risk band and contract type.\n", "# Business use: helps identify whether high-risk customers are also high-paying customers.\n", "pivot_4 = pd.pivot_table(\n", " df,\n", " index='Risk_Band',\n", " columns='Contract',\n", " values='MonthlyCharges',\n", " aggfunc='mean'\n", ").round(2)\n", "pivot_4" ] }, { "cell_type": "code", "execution_count": 36, "id": "6c363e1e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Risk_BandLow RiskMedium RiskHigh Risk
Customer_Value_Group
Low Value0.1570.4920.767
Medium Value0.0860.5000.743
High Value0.0980.4770.695
Very High Value0.1020.438NaN
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" ], "text/plain": [ "Risk_Band Low Risk Medium Risk High Risk\n", "Customer_Value_Group \n", "Low Value 0.157 0.492 0.767\n", "Medium Value 0.086 0.500 0.743\n", "High Value 0.098 0.477 0.695\n", "Very High Value 0.102 0.438 NaN" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Pivot 5 summarizes average churn probability by customer value group and risk band.\n", "# Business use: helps prioritize high-value customers who may need urgent retention action.\n", "pivot_5 = pd.pivot_table(\n", " df,\n", " index='Customer_Value_Group',\n", " columns='Risk_Band',\n", " values='Churn_Probability',\n", " aggfunc='mean'\n", ").round(3)\n", "pivot_5" ] }, { "cell_type": "markdown", "id": "d4bb1471", "metadata": {}, "source": [ "## 9. Reusable Pivot Function\n", "\n", "This function lets the analyst create custom pivot tables by changing the row field, column field, metric, aggregation type, filters, sorting order, and number of rows displayed." ] }, { "cell_type": "code", "execution_count": 37, "id": "fb36276d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Risk_BandLow RiskMedium RiskHigh RiskTotal
Contract
Month-to-month30311107152128
One year493460539
Two year42900429
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" ], "text/plain": [ "Risk_Band Low Risk Medium Risk High Risk Total\n", "Contract \n", "Month-to-month 303 1110 715 2128\n", "One year 493 46 0 539\n", "Two year 429 0 0 429" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This reusable function gives notebook users a flexible pivot table tool without depending on Jupyter widgets.\n", "def make_pivot(data, row='Contract', column='Risk_Band', metric='customerID', agg='count', filters=None, sort_by='Total', ascending=False, top_n=20):\n", " temp = data.copy()\n", " filters = filters or {}\n", "\n", " # Human note: Apply simple exact-match filters such as InternetService = Fiber optic.\n", " for field, value in filters.items():\n", " if value not in [None, '', 'All']:\n", " temp = temp[temp[field] == value]\n", "\n", " # Build the pivot table using the selected row, column, metric, and aggregation.\n", " pv = pd.pivot_table(temp, index=row, columns=column, values=metric, aggfunc=agg, fill_value=0)\n", "\n", " # The Total column supports quick sorting and easier review.\n", " pv['Total'] = pv.sum(axis=1) if agg in ['count','sum'] else pv.mean(axis=1)\n", "\n", " # Sort either by the calculated Total column or by the row index.\n", " if sort_by == 'Total':\n", " pv = pv.sort_values('Total', ascending=ascending)\n", " else:\n", " pv = pv.sort_index(ascending=ascending)\n", "\n", " return pv.head(top_n)\n", "\n", "# This example filters to Fiber optic customers and summarizes them by contract and risk band.\n", "custom_pivot = make_pivot(\n", " df,\n", " row='Contract',\n", " column='Risk_Band',\n", " metric='customerID',\n", " agg='count',\n", " filters={'InternetService': 'Fiber optic'},\n", " sort_by='Total',\n", " ascending=False,\n", " top_n=20\n", ")\n", "custom_pivot" ] }, { "cell_type": "markdown", "id": "f7dacab5", "metadata": {}, "source": [ "## 10. Interactive Pivot Dashboard Inside Jupyter\n", "\n", "This dashboard is interactive and works inside the notebook, but it does not use `ipywidgets`. Use the controls to filter, select rows and columns, choose metrics, aggregate values, sort results, and limit the number of rows displayed." ] }, { "cell_type": "code", "execution_count": 38, "id": "9ffd048c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "Customer Churn Interactive Pivot Dashboard\n", "

Customer Churn Interactive Pivot Dashboard

No ipywidgets required. Use filters, row/column selectors, metric, aggregation, sorting, and Top N controls to build pivot views directly in Jupyter or browser.

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Interactive Filters and Pivot Builder

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Pivot Table

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Graph: Pivot Row Totals

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Selected Customer Data

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\n", "" ], "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The dashboard below uses pure HTML and JavaScript, not ipywidgets.\n", "# This avoids the VBoxModel error and keeps the dashboard portable across JupyterLab versions.\n", "from IPython.display import HTML\n", "\n", "html_dashboard = r\"\"\"\n", "Customer Churn Interactive Pivot Dashboard\n", "

Customer Churn Interactive Pivot Dashboard

No ipywidgets required. Use filters, row/column selectors, metric, aggregation, sorting, and Top N controls to build pivot views directly in Jupyter or browser.

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Interactive Filters and Pivot Builder

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Pivot Table

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Graph: Pivot Row Totals

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Selected Customer Data

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\n", "\"\"\"\n", "HTML(html_dashboard)" ] }, { "cell_type": "markdown", "id": "18165dda", "metadata": {}, "source": [ "## 11. Export Client Deliverables\n", "\n", "This final section saves the client-ready outputs. The exported files can be used in reports, dashboards, follow-up analysis, or future model deployment." ] }, { "cell_type": "code", "execution_count": 39, "id": "96a8c6ed", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Export complete. Check the client_outputs folder.\n" ] } ], "source": [ "# The output folder keeps all client deliverables organized in one place.\n", "output_dir = Path('client_outputs')\n", "output_dir.mkdir(exist_ok=True)\n", "\n", "# Export customer-level scores so retention teams can review and prioritize customers.\n", "risk_scores.to_csv(output_dir / 'customer_churn_risk_scores_all_customers.csv', index=False)\n", "\n", "# Export model metrics for technical review and documentation.\n", "model_metrics.to_csv(output_dir / 'customer_churn_model_comparison_metrics.csv', index=False)\n", "\n", "# Export all pivot tables so they can be opened in Excel or used in reporting.\n", "pivot_1.to_csv(output_dir / 'pivot_1_customer_count_by_contract_and_risk_band.csv')\n", "pivot_2.to_csv(output_dir / 'pivot_2_avg_churn_probability_by_payment_and_contract.csv')\n", "pivot_3.to_csv(output_dir / 'pivot_3_actual_churn_rate_by_tenure_and_internet.csv')\n", "pivot_4.to_csv(output_dir / 'pivot_4_avg_monthly_charges_by_risk_and_contract.csv')\n", "pivot_5.to_csv(output_dir / 'pivot_5_high_value_customer_risk.csv')\n", "\n", "# Save the trained model so it can be reused later for scoring new customer data.\n", "joblib.dump(best_model, output_dir / 'customer_churn_prediction_model.joblib')\n", "\n", "# Save the standalone HTML dashboard for clients who want to open the dashboard outside Jupyter.\n", "with open(output_dir / 'customer_churn_interactive_pivot_dashboard.html', 'w', encoding='utf-8') as f:\n", " f.write(html_dashboard)\n", "\n", "print('Export complete. Check the client_outputs folder.')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 5 }