Project 4: Chicago West Nile
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README.md

proj4_group_repo

Project 4: Group 2 - Predicting Chicago WNV

EDA:

  1. EDA: Basic Basic stats.
  2. EDA: Map Maps of trap results by year.
  3. EDA: Graphs Plots of trap results over time.
  4. EDA: Weather Correlation with weather and WNV.

Model and Predictions

Cleaning

  1. Weather data prep

Feature Engineering

  1. Engineering weather features
    • Rolling averages
    • Combine same-day trap results
  2. Location weighting
  3. Clustering
  4. Combine weather and train datasets
  5. Interaction effects

Munging

  1. Undersampling
  2. Feature selection
    • Get dummies
    • Scaling
    • Concat
    • Omit columns not in test dataset

Modeling

  1. Train test split
  2. Modeling
  3. Predict

Prediction Reader Sanity Check

  • Plots WNV predictions over time and map.

Assets

All datasets for project (from Kaggle download).

Submission

All Kaggle submission .csv.


Viz: https://public.tableau.com/profile/sam.c7069#!/vizhome/ChicagoWNV_0/Map-ChicagoWNV?publish=yes

Slides: https://docs.google.com/presentation/d/16lK1tgRsx71pVeUpVn1VS1ZqgT3O418iS_QGK2pM940/edit?usp=sharing