Package: cab-dynamic-pricing
Easy CabsEasy Cabs is a ML-assisted web-based application which helps you in getting the dynamic pricing of Uber and Lyft cabs. The user enters the source and destination. Easy Cabs converts that to latitude, longitude, gets the weather information and predicts the estimated price for your rides using machine learning. The user can then make a decision on taking a cost-optimized cab.BackgroundUber and Lyft account for the major market capitalization for offering cab services on an app. But these prices are not constant like public transportation. They are greatly affected by the demand and supply of rides at a given time. So what exactly drives this demand? Some of the factors include weather changes, rush hours and location. EasyCabs takes into account these factors and tries to replicate a prototypical version of the pricing and surging microservices for these apps. It also offers an opportunity to retrain models using a feedback loop as a separate microservice.AuthorsNayantara MohanRohit LokwaniShubha Changappa PalachandaProject Distributioncab-dynamic-pricing/
  |- README.md
  |- cab-dynamic-pricing/
    |- __init__.py
    |- configuration_files/
      |- software_configuration_format.py
    |- database/
      |- users.csv
    |- feedback_app/
      |- training_testing_data/
        |- lyft_test_mlr.csv
        |- lyft_train_mlr.csv
        |- testing_surge_price_classifier_df.csv
        |- training_surge_price_classifier_df.csv
        |- uber_test_mlr.csv
        |- uber_train_mlr.csv
      |- dynamic_price_model_training.py
      |- surge_model_training.py
      |- train_models.py
    |- model_scripts/
      |- dynamic_pricing_regression_inference.py
      |- surge_classification_inference.py
    |- model_weights/
      |- lyft_mlr_model.sav
      |- surge_classification_rf_model.sav
      |- uber_mlr_model.sav
    |- templates/
      |- login_background.jpg
      |- login_index.html
      |- template_page2.html
    |- tests/
      |- test_app.py
      |- test_dynamic_pricing_model_training.py
      |- test_dynamic_pricing_regression_inference.py
      |- test_geospatial_information.py
      |- test_lyft.py
      |- test_software_configuration_format.py
      |- test_surge_classification_inference.py
      |- test_surge_price_model.py
      |- test_train_models.py
      |- test_uber.py
      |- test_user.py
      |- test_weather_information.py
    |- utils/
      |- geospatial_information.py
      |- lyft.py
      |- uber.py
      |- user.py
      |- utils.py
      |- weather_information.py
    | - app.py
  |- data/
      |- cab_rides.csv.zipe
      |- weather.csv
  |- documentation/
      |- api_documentation.md
      |- component_specification.md
      |- functional_specification.md
      |- screenshots/
  |- example
      |- example.md
      |- screenshots/
  |- .coveragerc
  |- .gitignore
  |- LICENSE
  |- requirements.txt
  |- setup.pyDataLink to the Data SetFeatures extracted from the dataset: -cab_type : Uber or Lyft.clouds : presence or absence of clouds.destination : name of the destination in words.humidity : humidity in percentage.location : location of the place where the weather is recorded.pressure : atmospheric pressure in millibar.price : price estimate for the ride in USD.rain : rain in inches for the last hour.name : type of the car specified, eg. X, XL.source : name of the source in words.surge_multiplier : 5 unique values mentioned.temp : temperature in Fahrenheit.time_stamp : start of the cab journey in epoch units.wind : wind speed in miles per hour.Note: The data currently covers locations in Boston. Hence, to get relatively accurate estimates, the default models are bound to work the best for Boston locations.InstallationMethod 1: Cloning the Github Repository:Clone the repogitclonehttps://github.com/rohitl17/cab-dynamic-pricing.gitCreate a virtual environment in the root of the repopython-mvenvvenvsourcevenv/bin/activateIf you're using Anaconda, create and activate a new conda environment. For conda runconda create --name cabdynamicpricing
conda activate cabdynamicpricingInstall the dependencies from the requirements.txt file using the below:python-mpipinstall-rrequirements.txtMethod 2: Installing the package using the pip command:Run the following command to install the applicationpipinstallcab-dynamic-pricingInstall the dependencies from the requirements.txt file using the below:python-mpipinstall-rrequirements.txtUsage and OutputTo see how to use the package to get the dynamic pricing of Uber and Lyft, refer to theexample file