Data Crushers

Inspiration

Our inspiration stemmed from the desire to harness the valuable dataset provided by Singlife for insightful data analyses. We aimed to leverage this data to offer feasible and long-term solutions that could enhance Singlife's customer purchase rate.

What it does

Our project focuses on addressing the challenge Singlife faced – customer hesitancy in signing policies. We conducted in-depth analyses on potential contributing factors to customer drop-offs, such as income, household size, and occupation risk.

How we built it

Using Python, we collaborated on a GitHub repository. We employed pandas to clean the data, utilizing k-nearest neighbor and ratio methods for handling null values. We used pandas and matplotlib to create informative charts analyzing demographics, factors affecting customer experience, and contributors to customer drop-offs. Building a predictive model for customer satisfaction and conversion rate, we achieved an impressive 92% accuracy.

Challenges we ran into

We encountered challenges in selecting the optimal method for filling null data, particularly when dealing with a significant amount of missing data. Integrating our machine learning model with our data analyses posed another hurdle.

Accomplishments that we're proud of

We successfully completed the entire datathon within a tight timeframe. Our achievement lies in navigating the entire data analytics process – from cleaning and analyzing data to building a predictive model and ensuring its accuracy.

What we learned

The datathon provided invaluable learning experiences. We gained expertise in executing a comprehensive data analytics workflow, from data cleaning to extracting relevant information for analysis and visualization, culminating in the development of a predictive model.

What's next for Untitled

Moving forward, we plan to integrate our solutions into Singlife's application process, streamlining personalized communication to cater to specific customer groups. Our aim is to contribute to a more seamless and satisfying customer experience for Singlife.