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https://github.com/chrispsang/customerchurnanalysis

Predicting customer churn using a RandomForestClassifier with detailed EDA, model evaluation, and visualization. Includes a Tableau dashboard for interactive insights.
https://github.com/chrispsang/customerchurnanalysis

customerchurn data-analysis data-visualization datapreprocessing machine-learning python scikit-learn tableau

Last synced: 3 months ago
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Predicting customer churn using a RandomForestClassifier with detailed EDA, model evaluation, and visualization. Includes a Tableau dashboard for interactive insights.

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README 

        
# Churn Analysis Project



This repository contains a sample project for analyzing customer churn in a subscription service. The project uses Python for data analysis and model building, and Tableau for data visualization.



## Table of Contents



- [Project Overview](#project-overview)

- [Dataset](#dataset)

- [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)

- [Model Building](#model-building)

- [Evaluation](#evaluation)

- [Tableau Visualizations](#tableau-visualizations)



## Project Overview



The goal of this project is to predict customer churn for a subscription service. The analysis involves:



1. Data Preprocessing

2. Exploratory Data Analysis (EDA)

3. Model Building and Training

4. Model Evaluation

5. Exporting Data for Tableau Visualization



## Dataset



The dataset used for this project is synthetically generated and consists of the following features:



- `CustomerID`: Unique identifier for each customer

- `Gender`: Gender of the customer

- `Age`: Age of the customer

- `Tenure`: Number of months the customer has been with the company

- `SubscriptionPlan`: Subscription plan of the customer (Basic, Standard, Premium)

- `MonthlyCharges`: Monthly charges for the customer

- `Churn`: Whether the customer has churned (0 = No churn, 1 = Churn)

- `TotalCharges`: Total charges for the customer (calculated as `Tenure` * `MonthlyCharges`)



## Exploratory Data Analysis (EDA)



The EDA section of the code includes:



- **Churn Count Visualization**: This plot shows the distribution of churned vs. non-churned customers, providing a quick look at the imbalance in the dataset.

- **Age Distribution Visualization**: A histogram that displays the age distribution of customers, helping to understand the age range and common age groups within the dataset.

- **Monthly Charges by Subscription Plan Visualization**: A box plot that illustrates the distribution of monthly charges across different subscription plans, highlighting the variations in charges among the plans.

- **Correlation Matrix Visualization**: A heatmap showing the correlation between different numerical features, which helps in identifying the relationships and dependencies among the features.



## Model Building



The model building process includes:



- Train-test split with stratification

- Feature scaling

- Training a RandomForestClassifier



## Evaluation



The model evaluation includes:



- **Classification Report**: Provides precision, recall, and F1-score for the model, giving a detailed performance summary.

- **Confusion Matrix**: A matrix that shows the counts of true positives, true negatives, false positives, and false negatives, helping to evaluate the classification accuracy.

- **ROC AUC Score**: The ROC AUC score is used to measure the model's ability to distinguish between classes.

- **ROC Curve Visualization**: A plot of the True Positive Rate (TPR) against the False Positive Rate (FPR), showing the performance of the classification model at various threshold settings.

- **Feature Importance Visualization**: A bar plot that ranks the features based on their importance in the model, indicating which features have the most influence on predicting churn.



## Tableau Visualizations



You can view the Tableau visualizations for this project [here](https://public.tableau.com/views/ChurnAnalysisDashboard_17225321250850/Dashboard1?:language=en-US&publish=yes&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link).



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