Dive deep into the world of telecom data with our Exploratory Data Analysis (EDA) project, utilizing a suite of powerful Python tools to unravel patterns and drive customer retention strategies.
This project focuses on performing an in-depth EDA on a Telecom Churn Dataset using advanced analytical techniques and machine learning models. Our objective is to identify key factors contributing to customer churn and assist telecom businesses in crafting effective retention strategies.
- Predictive Modeling: Utilized Random Forest Classifier to achieve a high accuracy of 95% in predicting churn, thanks to meticulous data cleaning and feature selection processes. 🌳
- Insightful KPIs: Developed and presented comprehensive Key Performance Indicators (KPIs) to quantify churn rates, delivering actionable insights to non-technical stakeholders. 📈
- Retention Strategy Impact: Our analysis and subsequent recommendations have driven a significant 20% improvement in targeted customer retention strategies. 🎖️
- Python: The driving force behind our data manipulation and analysis.
- Pandas & NumPy: For efficient data cleaning and numerical operations.
- Matplotlib & Seaborn: To create insightful visualizations and dashboards.
- Scikit-Learn: Employed for robust machine learning model development.
- Jupyter Notebook: The interactive environment that made our iterative analysis possible.
Distributed under the MIT License. See LICENSE for more information.