Project 4: Group 2 - Predicting Chicago WNV
- EDA: Basic Basic stats.
- EDA: Map Maps of trap results by year.
- EDA: Graphs Plots of trap results over time.
- EDA: Weather Correlation with weather and WNV.
Model and Predictions
- Weather data prep
- Engineering weather features
- Rolling averages
- Combine same-day trap results
- Location weighting
- Combine weather and train datasets
- Interaction effects
- Feature selection
- Get dummies
- Omit columns not in test dataset
- Train test split
Prediction Reader Sanity Check
- Plots WNV predictions over time and map.
All datasets for project (from Kaggle download).
All Kaggle submission .csv.