PredUrTick

Car parking ticket prediction for the city of New York.

All Dataset Files

Link:https://drive.google.com/open?id=1iOYIlAlMoQAyVy0fQGf-4TeJ_4utU4s6
Youtube Link to presentation: https://youtu.be/zvAsq2L9cuQ

Files Information

1. To Run Application GUI:
   File: Final Gui Model Implementation.ipynb
   Data: Dataset/ParkingData_Month_Time_Week.csv
   
   

2. EDA Files:
   Folder: EDA + Data Preprocessing/
   
   Files:
   

   1. EDA-1.ipynb
      
   2. EDA-2.ipynb
      Data: Dataset/Sample_data_2017.csv
      
      

3. Data Visualization:
   Folder: EDA + Data Preprocessing/
   
   Files:
   

   1. Location based heat maps.ipynb
      Heatmaps plotted for precinct-based and street-based analysis on the map
      
   2. Time based heat maps.ipynb
      The time and date is divided over hours, daily, week and monthly data.
      Heatmap with respect to time plotted for hourly, daily, weekly, monthly.
      The heatmaps are quite interactive and they show variation over time.
      

Data: Dataset/Sample_NaStreet_Removed.csv

Folder: Maps
Has all the maps generated during data visualization

Note: Internet Connection is necessary for the maps to work

4. Data Pre-Processing:
   
   Folder: EDA + Data Preprocessing
   
   Files:
   

   1. Get Coordinates.ipynb - to get latitude, longitude from street name using google geoAPI
      Data: Dataset/Sample_data_2017.csv
      
      
   2. Time based dataset.ipynb - to add time slot column to the data
      Data: Dataset/Sample_data_2017.csv
      
      
   3. Get data based on bounding box.ipynb - to get data only belonging to the state of New York
      Data: Dataset/Sample_NaStreet_Removed.csv
      
      
   4. Parking Ticket Dataset.ipynb - to make month and days into numbers and prepare final dataset for model implementation
      Data: Dataset/bounded box data.csv
      
      

5. Performance Metrics:
   
   File: Performance Metrics for all cases
   Data: Dataset/ParkingData_Month_Time_Week.csv
   

6. Kmeans:
   
   Folder: K means/models
   File: K means/K Means clustering to predict prone areas.ipynb
   Dataset: Dataset/ParkingData_Clustered_Kmeans.csv
   Note: The code for generating weights has been commented out. Download the models and dataset to make predictions.