Overview
The project involved an in-depth analysis of Ministri’s polling data spanning six years to discern trends and patterns. Utilizing a variety of graphs, we visualized these insights for enhanced understanding and interpretation.
### **Model Development**
Leveraging a preprocessed dataset and a robust infrastructure comprising a 4 x A10 GPU cluster, we developed a sophisticated predictive model. This model, with 7M parameters, employed time-series Transformer architecture. Its primary objective was to forecast the next-hour polling percentage, gender distribution, and age distribution across 11 Regions and 63 Wilayat.
### **Data Pipeline**
A streamlined data pipeline was crucial for seamless data processing. We implemented Debezium, Kafka, and Pyspark to achieve this. Debezium facilitated the real-time streaming of data from the database, while Kafka ensured efficient data transmission. Pyspark played a pivotal role in data transformation, enabling us to preprocess and prepare the data for analysis.
### **Integration and Presentation**
To democratize the insights gleaned from our analysis, we integrated graphical data generated by Pyspark with a talking Avatar. This fusion of visual data and interactive presentation was channeled through a mobile application. By employing this innovative approach, we aimed to make election insights accessible and engaging for the populace.
### **Conclusion**
Through meticulous analysis, robust model development, and innovative presentation techniques, our project successfully illuminated election trends and facilitated informed decision-making. This endeavor underscores the power of data-driven insights in shaping the democratic process.