NYC rat infestation predictor🌆 🐀
The project aims to develop a system to predict and manage rat infestations in Manhattan using data from the NYC Open Data repository, heavily dependent on GIS data. It involves analyzing rat inspection data, geographic locations of buildings and parks, restaurant inspections, and subway line locations. It outlines methods for creating a building proximity matrix, loading and organizing data, and visualizing the data on maps. Additionally, the project describes techniques for finding the centers of city blocks, creating a city block network, and employing various clustering and regression methods to predict infestation risks. The project also considers the proximity of buildings to subway lines and public parks as factors in rat activity.
Libarires used: Google Drive, Math, NumPy, Pandas, Matplotlib, Folium, GeoPandas, Pyproj, Contextily, Seaborn, Shapely, NetworkX, Datetime, Scipy, Branca, Plotly, Sklearn Linear Model, Sklearn Model Selection, Sklearn Metrics, Sklearn SVM, Sklearn Decomposition, Sklearn Preprocessing, Sklearn Pipeline.