A financial services firm needed to better predict market trends and customer churn by leveraging their vast, underutilized datasets. Existing BI tools lacked predictive capabilities and geospatial insights.
We built a Python-based analytics platform using TensorFlow for ML model development and Flask for API delivery. Data from various sources was aggregated into a data lake on AWS. Power BI and Grafana were used for interactive dashboards, with ArcGIS Online integration for geospatial visualizations.
Handling and processing large volumes of heterogeneous data efficiently.
Utilized Apache Spark for distributed data processing within the AWS environment and optimized ETL/ELT pipelines.
Ensuring model explainability and building trust in predictive insights among business stakeholders.
Implemented model interpretability techniques (e.g., SHAP values) and conducted workshops to explain model workings and limitations.
The Predictive Analytics & BI Platform, delivered with a clear scope under a Fixed Price model, empowered the client with advanced foresight, transforming their ability to anticipate market changes and retain customers.