/Trasportation-Analytics-MongoDB-Atlas-Compass

Implemented trucking and transportation analytics using MongoDB Atlas and Compass.

Primary LanguageJupyter NotebookMIT LicenseMIT

Trasportation Analytics using MongoDB Atlas and Compass

Business Overview

MongoDB is a document database designed for highly accessible and scalable web applications. Its flexible schema technique is popular among agile development teams, enabling quick application launches without the hassle of setting up a traditional relational database. MongoDB's document-oriented architecture simplifies data storage for structured and unstructured data, with a format similar to JSON.

Objective

This project focuses on exploring MongoDB, a NoSQL database, its basic architecture, data modeling, querying, aggregations, and tools for tasks like Data Analytics, ETL, and Data Visualization.

Dataset Description

The project uses a transportation dataset, including information about truck drivers, routes, cities, trucks, and historical truck schedules. The dataset is organized into several tables:

  1. driver_details

    • driver_id: Unique identifier for each driver
    • name: Name of the truck driver
    • gender: Gender of the truck driver
    • age: Age of the truck driver
    • experience: Experience of the truck driver in years
    • driving_style: Driving style of the truck driver (conservative or proactive)
    • ratings: Average rating of the truck driver on a scale of 1 to 10
    • vehicle_no: Number of the driver’s truck
    • average_speed_mph: Average speed of the truck driver in miles per hour

  2. truck_details

    • truck_id: Unique identification number of the truck
    • truck_age: Age of the truck in years
    • load_capacity_pounds: Loading capacity of the truck in pounds
    • mileage_mpg: Mileage of the truck in miles per gallon
    • fuel_type: Fuel type of the truck

  3. city_details

    • city_id: Unique identification number of the city
    • city_name: Name of the city
    • lat: Latitude
    • lon: Longitude

  4. route_details

    • route_id: Unique identifier of the routes
    • origin_city_id: City identification number for the origin city
    • destination_city_id: City identification number for the destination
    • distance(Miles): Distance between the origin and destination cities in miles
    • average_hours: Average time needed to travel from the origin to the destination in hours

  5. truck_schedule_data

    • truck_id: Unique identifier of the truck
    • route_id: Unique identifier of the route
    • departure_date: Departure DateTime of the truck
    • estimated_arrival: Estimated arrival DateTime of the truck
    • delay: Binary variable indicating if the truck’s arrival was delayed (0 for on-time arrival, 1 for delayed arrival)

Tech Stack

  • Language: Python
  • Package: PyMongo
  • Services: MongoDB Atlas, MongoDB Compass