Journey to School


journey to school



Journey to school


In this project, we will design a model that predicts how do you decide goining to school. You do not need any AI knowledge.




The problem

Whether you go walking, or by bus, a bicycle, or by a car; students journey to school can be in different ways. In this project, we will create a model that uses data about you to predict how you transport to school.


learning outcomes

From the knowledge you gain from this project, you will grasp how (to):

  • Train a computer to make predictions
  • Predictive analytics works to detect patterns in structured data.
  • Use survey results to train a predictive model.


The data

The dataset for this project is tabular data from the dataset library, "Journey to School" dataset.



Project steps:

1.Meet your AI Identity

Meet your AI Identity. You will teach your AI how complete the tasks as a human would. In due time, your AI Identity will grow more intelligent as you train it some more to take on different tasks with multiple projects. Eventually -in the future- just maybe you will be able to transfer your AI transformer’s intellect into a physical robot you can interact with in the real world.  



Begin every project by naming the project so you can keep track of what project it is exactly. Every project involves the steps of dataset preprocessing, model building, evaluation, and deployment.

By creating projects, working with others becomes more convenient and easier.

Click CREATE once you have added a project name and description. 



In this step, you will need to select one of the ML models in order to solve your problem. You will find a computer vision model (Make Me See), Natural Language Processing model (Make Me Read), Voice Recognition (Make Me Hear), and a tabular model (Make Me Count). In our case, you will need to train your AI transformer to how to read sentences, therefore, you need to select MAKE ME READ. 

More AI capabilities (models) will be added to the platform, so stay tuned! These models will help you to accelerate the process of creating a trained AI model that solves your problem, and thus export into your app to start using it!




This is the most important step in any ML project. In fact, data preprocessing is 70-80% of any ML project. In this section, you will need to manually collect your  from reliable resources, such as Kaggle, or import the "Journey to school" dataset from our data library.Use the “Tabular Data” dataset from the dataset library to collect the data you need for this project.




Use the data you have gathered to train your AI transformer. “Training” your model refers to teaching it to predict your means of transport. To do this, click TRAIN ME. Be patient for a moment while this process goes on.   



Right now, and after you completed training your model, you are ready to test it using a new sample of values. You can test your model using random values from the testing dataset (see point 4). Just make sure that you dont use an image from the dataset that you used in the training part, otherwise, you are cheating! Upload or import the sample image to assess the performance in the preview section. If you like the confedence score, you can export your model. Otherwise, go back to the "collect data" step and check whether you have used suffient number of images in the training dataset. 



If you are happy with the testing results, you can "Export" your ML model using our API into your own websiteapplication or even to your robot or any machine! If you are not happy with the confidence score, you will need to check your training data classes again, and make sure that you have clean and sufficient number of examples for each data class. Keep in mind the concept of GIGO (garbage in, garbage out) which means that the quality of your ML model output is determined by the quality of your input, i.e. training dataset.