Problem

As a team of aspiring data scientists, we lacked hands-on experience with R programming. We had completed a certificate course on data science with R programming but still felt unprepared to tackle real-world projects.

We needed a way to gain practical experience and build our confidence in using R programming for data analysis.

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Solution

We decided to create a project that would allow us to apply our knowledge of R programming to real-world data analysis. We chose a dataset from Kaggle on the topic of customer churn for a telecommunications company.

Our team worked together to clean and preprocess the data, perform exploratory data analysis, and build predictive models using various machine learning algorithms. We also created visualizations to help communicate our findings.

Impact

Improved our understanding and proficiency in R programming by applying it to a real-world problem
Gained experience in working collaboratively as a team on a data science project
Increase in accuracy of predicting customer churn by X%

"Quote of how incredible of an impact this had."

Team

Team Member #1 - Data Scientist
- Cleaned and preprocessed the dataset, performed exploratory data analysis, built predictive models using various machine learning algorithms
Team Member #2 - Data Analyst
- Created visualizations to help communicate our findings, assisted with data cleaning and preprocessing

Aspiring Data Scientists Tackle Customer Churn