Areej Hatem Eweida
Data Science Fellows June 2020 Cohort
in the project we aim to accomplish the following:
- Identify and visualize which factors contribute to customer abandonment:
- Build a prediction model that will perform the following:
- Classify if a customer is going to abandon or not.
- Preferably and based on model performance, choose a model that will attach a probability to the churn to make it easier for customer service to target low hanging fruits in their efforts to prevent churn.
- Come up with interesting insights and findings regarding why a customer would abandon to help improve the product.
Challenges (at least two)
- Working remotely especially at first when I wasn’t very familiar with the data and the frameworks.
- Dealing with a large amount of data for the first time.
Achievements (according to KPIs)
- Analyzed the business case, determined the required variables, and used advanced queries in order to create the database.
- Experienced with common machine learning algorithms like SVM, random forest and K-means and different neural network architectures.
- Implemented preprocessing techniques for models to reduce loss and performed grid search for a better choice of hyperparameters. Reached test accuracy of unseen data above 85%.
- Helped the company develop strategic plans based on predictive modeling and findings.
Future project development
- The current model could be used by the product team for different applications.
- Further improvements can be done with the predictive model like collecting more features of the users and feed the model with more data.