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Local Chapter project

Predict Train Delays in Germany using Machine Learning

Machine Learning
Start date
January 8, 2023
Finish date
March 30, 2023
Project status
completed
Predict Train Delays in Germany using Machine Learning

Challenge background

Studies have shown that about 30% of the donations made in churches during church services through the regular tithe and offering are remitted to the church authority by those church workers in charge of it. This act of treasury theft has made most churches unable to run their activities of churches effectively. Efforts made by some church heads to combat this ugly menace have yielded little or no result, hence the need for a computer vision approach to solving the problem.

Travelling by train around Germany and even to some countries across Europe is an easy, quick and infact, a comfortable option. This is because it is an incredibly fun way to explore the continent. There’s less hassle, more comfortable seats, more ease of moving around, often better views, and more control over your environment.

Project timeline

  1. 1

    Week 1

    Project Objective discussion / Data Collection

  2. 2

    Week 2

    Data Sourcing / Data Pre-Processing

  3. 3

    Week 3

    Data Analysis / Exploratory Data Analysis

  4. 4

    Week 4

    Features and Model selection / Modelling

  5. 5

    Week 5

    Prediction and Forecasting

  6. 6

    Week 6

    Testing model with test data

What you'll learn

1. Collection of Data. 2. Data Cleaning. 3. Data Analysis. 4. Build Machine Learning 5. Develop API

Get involved

What to expect from a Local Chapter project

First project

  • Welcomes beginners and experienced practitioners.
  • Focuses on education and collaborative delivery.
  • Produces open-source project work.

Benefits

  • Address a significant real-world problem with your skills.
  • Build your project portfolio.
  • Demonstrate your work to organizations and project partners.

Requirements

  • Working English communication.
  • A learning mindset.
  • Commitment to collaborative project work.