Telecom Churn Analysis and Prediction

Data Collection and Exploration:

We loaded a dataset with 7,043 records, encompassing demographic information (like gender and age) and customer service details (such as internet services and technical support).

? Data Cleaning:

We addressed missing values and transformed data types to facilitate analysis, ensuring the accuracy of the data used.

Data Analysis:

We employed techniques such as statistical analysis and visualization to understand customer behavior. The results revealed that 26.5% of customers decided to churn, influenced by factors like contract type and online security services.

Demographic Insights:

Notably, senior citizens exhibited a higher churn rate, indicating a need for special attention from marketing and support teams.

Visualizations:

We created a series of charts, including pie charts and bar graphs, to present the findings clearly and effectively.

? What I Learned:

Deep data analysis can uncover unexpected behavior patterns, aiding companies in enhancing customer retention strategies.

What's Next?

I plan to leverage these insights to develop a predictive model that helps companies identify customers at risk of churning and customize offers to meet their needs.