ranayalcink/waze-user-churn-binary-classification

Applied advanced analytics to forecast Waze user churn, employing logistic regression, XGBoost, and comprehensive statistical analysis. Proficiently utilized Python, Scikit-learn, and StatsModels, demonstrating a keen ability to extract actionable insights.

Waze User Churn Prediction Project

Business Problem/Overview

The primary goal of this project is to forecast user churn for Waze, directly addressing the pivotal business challenge of mitigating churn, improving user retention, and catalyzing comprehensive business growth. Within this README, you'll find a concise overview of the project, insights into response, the impact, and a showcase of the demonstrated skills.

This project directly addresses a business challenge of predicting and preventing user churn. While the specific dataset is fictional, the methodologies, analyses, and tools employed are very much in line with real-world scenarios. This project reflects my skills as a data analyst by showcasing proficiency in diverse analytical techniques, strategic thinking aligned with business goals, effective communication through an executive summary, and a commitment to staying updated on the latest in data analytics. Ultimately, it's a tangible demonstration of how I can contribute to driving business success through data-driven insights.

Response

Data Exploration and Visualization

Utilized Python, Pandas, NumPy, Matplotlib, and Seaborn for comprehensive data exploration and visualization, extracting valuable insights to inform subsequent analysis. Employed exploratory techniques such as scatter plots, histograms, and heatmaps to identify patterns and outliers.

Statistical Analysis

Applied statistical methods and hypothesis testing to uncover patterns and trends within the dataset, providing a robust foundation for further modeling. Conducted hypothesis tests to validate assumptions and gain statistical confidence in the findings.

Predictive Analysis and Model Building

Conducted predictive analysis through the construction of regression models, including logistic regression, XGBoost, decision tree, and random forest, utilizing both Scikit-learn and StatsModels libraries. Employed feature engineering to enhance model performance and interpretability, showcasing a strong focus on predictive analytics.

Executive Summary

Prepared a detailed executive summary in PDF format, consolidating key findings, model performances, and actionable recommendations for stakeholders. Effectively communicated complex technical concepts to non-technical audiences.

Impact

Insights derived from this project have the potential to significantly impact Waze's business by:

• Strengthening user retention strategies.
• Enhancing the overall user experience.
• Contributing to the sustainable growth of Waze.

Skills Demonstrated

• Data Exploration and Visualization: Proficient in Python, Pandas, NumPy, Matplotlib, Seaborn, and Tableau. Applied a variety of visualization techniques to convey insights effectively.

• Statistical and Predictive Analysis: Applied hypothesis testing, conducted descriptive statistics, and showcased a strong focus on predictive analytics through the construction of regression models.

• Model Building: Constructed regression model, XGBoost, decision tree, and random forest using Scikit-learn and StatsModels. Demonstrated expertise in feature engineering.

• Communication: Developed and presented an executive summary, effectively communicating complex findings to diverse stakeholders.

Tools Used

• Python Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, StatsModels, Scipy.

• Visualization Tool: Tableau.

Conclusion

This project showcases not only technical proficiency but also effective communication of complex data science concepts. The emphasis on both statistical and predictive analysis demonstrates the ability to provide actionable insights for driving business success.

This project is a part of the Google Advanced Data Analytics Certificate. Please note that the data used in this project is entirely fictional.

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Feel free to reach out if you have any comments, additional details to add, or if there are further opportunities for discussion. I'm open to any insights or feedback you may have.