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https://github.com/edochiari/waze-project

This project develops a churn prediction model to identify Waze users at high risk of stopping app usage. By analyzing user data, the model aims to reveal factors contributing to churn, helping Waze retain more users through targeted engagement strategies.
https://github.com/edochiari/waze-project

churned-users dataanalysis datacleaning hypothesis-testing jupyter-notebook machine-learning regression retained-users waze

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This project develops a churn prediction model to identify Waze users at high risk of stopping app usage. By analyzing user data, the model aims to reveal factors contributing to churn, helping Waze retain more users through targeted engagement strategies.

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README 

        

## Overview

This project provides an in-depth analysis and predictive modeling approach to support Waze's efforts to improve user retention. By examining churn-related data, this analysis aims to understand user behavior patterns and identify factors that contribute to monthly churn. Insights from this project will empower Waze leadership to make data-driven decisions to enhance user experience, proactively engage at-risk users, and support long-term growth.



## Project Goals

1. **Predict User Churn**: Develop and evaluate models to accurately predict which users are likely to churn, enabling Waze to focus retention efforts where they are needed most.

2. **Identify Key Drivers of Churn**: Pinpoint critical features such as app usage frequency, interaction patterns, and geographic location that influence user retention.

3. **Provide Actionable Insights**: Offer insights for Waze’s leadership to inform product development and enhance user engagement strategies.



## Deliverables

The final project deliverables include:



- **Model Evaluation**: Thorough assessment of model performance, including accuracy, precision, and recall, with an emphasis on interpreting metrics in the context of predicting churn.

- **Data Visualizations**: Clear visualizations to communicate churn trends and user patterns effectively, aiding stakeholders in understanding the factors that influence churn.

- **Feature Analysis**: Examination of which features most strongly impact churn likelihood, including possible underlying reasons (e.g., infrequent app usage or low engagement).

- **Retention Strategy Recommendations**: Suggestions for feature engineering, personalized user retention tactics, and other data-driven recommendations to enhance user experience and prevent churn.



## Tools and Libraries Used

- **Data Analysis and Visualization**: Pandas, NumPy, Matplotlib, Seaborn

- **Machine Learning**: Scikit-learn (for classification and regression models)

- **Notebook Environment**: Jupyter Notebook



## Project Structure

The project is organized as follows:



1. **Data Collection and Preparation**: Data gathering, cleaning, and processing to prepare a comprehensive dataset suitable for modeling and analysis.

2. **Exploratory Data Analysis (EDA)**: In-depth exploration of churn data, including user engagement trends, to identify significant factors that impact retention.

3. **Feature Engineering**: Creation of additional features to enhance model accuracy, such as time since last app usage, and user engagement metrics.

4. **Hypothesis Testing**: Statistical testing to validate findings and understand which factors have a significant influence on churn.

5. **Model Building and Evaluation**: Development of regression and classification models to predict churn and assess their performance on key metrics.

6. **Executive Summary**: A summary of findings and insights tailored for non-technical stakeholders, highlighting the potential impact on retention strategies.



## Conclusion

This project equips Waze leadership with valuable insights into churn behavior, offering a data-driven approach to optimize retention strategies and boost long-term user engagement. By combining robust data analysis with ethical considerations in model deployment, this project provides a responsible and impactful method for understanding and addressing user churn in a way that supports growth and enhances the user experience.



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