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.