DataStorm'21 - Winners
| #Deep Learning #Computer Vision #Generative AI #Adversarial Learning
Description
The competition pushed us to the limits of creativity and problem-solving, ultimately culminating in the development of a cutting-edge customer churn prediction model using complex telecom data. We embarked on this challenge by delving deep into data exploration and analysis, leading to meticulous data filtering and pre-processing. The art of feature engineering allowed us to extract valuable insights from the dataset, and we didn't stop there. We tested a range of models, from Random Forest to XGBoost, culminating in the implementation of a sophisticated two-stacked ensemble model. This experience was an invaluable lesson in the power of data-driven decision-making and the importance of adaptability in the ever-evolving field of data science.
Technologies
Methodologies
Data Exploration and Analysis Data Filtering Feature Engineering
Key Achievements
- Comprehensive Data Exploration: Thoroughly dissected the complex telecom dataset to uncover hidden patterns and insights.
- Effective Data Filtering: Pruned and cleaned the data, ensuring the highest data quality and relevance.
- Stacked Ensemble Model: Implemented an advanced two-stacked ensemble model to harness the strengths of multiple algorithms.