Work on clients’ data to help it understand the primary causes of unfulfilled requests as well as come up with solutions that recommend drivers locations that increase the fraction of complete orders.
We will try to answer some interesting question that cannot be answered by just analyzing observational data alone.
• Given drivers are recommended to move 1km every 30 mins in a selected direction, what happens to the number of unfulfilled requests?
• If we assume we know the location of the next 20% of orders within 5km accuracy, what happens to the number of unfulfilled requests?
• Had we changed the time requirements to drivers operating time in the past, what fractions of orders could have been completed?
• If I increased the number of drivers by 10% cumulative per month, what fraction of orders can be completed?
Work on clients’ data to help client understand the primary causes of unfulfilled requests as well as come up with solutions that recommend drivers locations that increase the fraction of complete orders. Since drivers are paid based on the number of requests they accept, the solution will help client business grow both in terms of client satisfaction and increased business.
- Drop columns with empty entries
- Drop rows with NaN entries
- Merge the Two tables
- Generate month, day, week day, and an hour from the trip start time column.
- Calculate the driver proximity to the order using trip origin and driver location when the driver got the order which is given in lat and lng in the second table.
- Calculate trip distance and trip duration and then trip speed.
- I also used the API from https://api.weatherbit.io/v2.0/history/daily? to get the weather at a given location and time-stamp. Public, school, regional and national holidays are calculated from the trip start time.
- plot driver distance vs acceptance rate
- plot latitude vs longitude of dirver location
- Used causalnex libary to build structural model
- generarated some graphs
- generated maps screenshots from this project are found here
Folder caointing information about structural model of the causalnex network and visualization
Caintains separate notebooks for the following purposes:
- EDA on separate datsets
- EDA on merged two dasets
- EDA and feature engineering on merged datasets.
- Causal Inference and Causal Graphs
- Contains methods and functions for data cleaning and data extraction tasks.
Locations of destination for orders
Locations of origin for orders
unit tests for the methods found in scripts directory
clone https://github.com/niyotham/Causal-Inference-Logistic-optimization.git
cd Causal-Inference-Logistic-optimization
pip install -r requirements.txt
- Finalize the causal inference model
- Do more feature engineering and visualizations
- Improving and implement Logistic optimization
- Collaborate with domain expert thoughts to create a more meaningful causal graph.
-
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
-
Please make sure to update tests as appropriate.