Zindi-New-User-Engagement

Introduction

1. Zindi is the first data science competition platform in Africa which hosts an entire data science ecosystem of scientists, engineers, academics, companies, NGOs, governments and institutions.​
2. The data which is provided to us is a part of Zindi user activity.​
3. Our task is to determine if a new user will be active in the upcoming month using the data of their previous months.​
4. This helps Zindi track the user activity and improve the platform.

Problem Definition To build a model that, given data of the user activity of month of sign up, can predict user activity for the upcoming month.

Objectives

1. To perform data analysis and identify criteria for what constitutes an active user.​

2. To build a model to predict whether the user will engage in the Zindi platform in the upcoming month, based on their activity in the previous months.

Proposed Methodology

1. Classify a user as active or inactive in a particular month. ​

• KMeans clustering​
• Criteria​

2. Approaches to solve this problem​

• Concatenate each month user activity as new columns​
• Data as sequence​
• Activity Based Grouping​

3. Model Building

Conclusion

• We performed data analysis to decide which features help determine user activity and how they affect user activity.​
• Using this information, we derived a criteria for classifying user as active or inactive.​
• Finally, we came up with approaches to solve this problem and built models to predict user activity in the upcoming month.